Statistical models which describe the relationship between a qualitative dependent variable (that is, one which can take only certain discrete values, such as the presence or absence of a disease) and an independent variable. A common application is in epidemiology for estimating an individual's risk (probability of a disease) as a function of a given risk factor.
An aspect of personal behavior or lifestyle, environmental exposure, or inborn or inherited characteristic, which, on the basis of epidemiologic evidence, is known to be associated with a health-related condition considered important to prevent.
The ratio of two odds. The exposure-odds ratio for case control data is the ratio of the odds in favor of exposure among cases to the odds in favor of exposure among noncases. The disease-odds ratio for a cohort or cross section is the ratio of the odds in favor of disease among the exposed to the odds in favor of disease among the unexposed. The prevalence-odds ratio refers to an odds ratio derived cross-sectionally from studies of prevalent cases.
A set of techniques used when variation in several variables has to be studied simultaneously. In statistics, multivariate analysis is interpreted as any analytic method that allows simultaneous study of two or more dependent variables.
Studies in which the presence or absence of disease or other health-related variables are determined in each member of the study population or in a representative sample at one particular time. This contrasts with LONGITUDINAL STUDIES which are followed over a period of time.
Studies which start with the identification of persons with a disease of interest and a control (comparison, referent) group without the disease. The relationship of an attribute to the disease is examined by comparing diseased and non-diseased persons with regard to the frequency or levels of the attribute in each group.
The application of STATISTICS to biological systems and organisms involving the retrieval or collection, analysis, reduction, and interpretation of qualitative and quantitative data.
The total number of cases of a given disease in a specified population at a designated time. It is differentiated from INCIDENCE, which refers to the number of new cases in the population at a given time.
Social and economic factors that characterize the individual or group within the social structure.
A graphic means for assessing the ability of a screening test to discriminate between healthy and diseased persons; may also be used in other studies, e.g., distinguishing stimuli responses as to a faint stimuli or nonstimuli.
Statistical formulations or analyses which, when applied to data and found to fit the data, are then used to verify the assumptions and parameters used in the analysis. Examples of statistical models are the linear model, binomial model, polynomial model, two-parameter model, etc.
Studies used to test etiologic hypotheses in which inferences about an exposure to putative causal factors are derived from data relating to characteristics of persons under study or to events or experiences in their past. The essential feature is that some of the persons under study have the disease or outcome of interest and their characteristics are compared with those of unaffected persons.
Age as a constituent element or influence contributing to the production of a result. It may be applicable to the cause or the effect of a circumstance. It is used with human or animal concepts but should be differentiated from AGING, a physiological process, and TIME FACTORS which refers only to the passage of time.
Studies in which subsets of a defined population are identified. These groups may or may not be exposed to factors hypothesized to influence the probability of the occurrence of a particular disease or other outcome. Cohorts are defined populations which, as a whole, are followed in an attempt to determine distinguishing subgroup characteristics.
In screening and diagnostic tests, the probability that a person with a positive test is a true positive (i.e., has the disease), is referred to as the predictive value of a positive test; whereas, the predictive value of a negative test is the probability that the person with a negative test does not have the disease. Predictive value is related to the sensitivity and specificity of the test.
Maleness or femaleness as a constituent element or influence contributing to the production of a result. It may be applicable to the cause or effect of a circumstance. It is used with human or animal concepts but should be differentiated from SEX CHARACTERISTICS, anatomical or physiological manifestations of sex, and from SEX DISTRIBUTION, the number of males and females in given circumstances.
The qualitative or quantitative estimation of the likelihood of adverse effects that may result from exposure to specified health hazards or from the absence of beneficial influences. (Last, Dictionary of Epidemiology, 1988)
Observation of a population for a sufficient number of persons over a sufficient number of years to generate incidence or mortality rates subsequent to the selection of the study group.
Inhaling and exhaling the smoke of burning TOBACCO.
Predetermined sets of questions used to collect data - clinical data, social status, occupational group, etc. The term is often applied to a self-completed survey instrument.
Elements of limited time intervals, contributing to particular results or situations.
A systematic collection of factual data pertaining to health and disease in a human population within a given geographic area.
Studies in which variables relating to an individual or group of individuals are assessed over a period of time.
The status during which female mammals carry their developing young (EMBRYOS or FETUSES) in utero before birth, beginning from FERTILIZATION to BIRTH.
The study of chance processes or the relative frequency characterizing a chance process.
A country in western Europe bordered by the Atlantic Ocean, the English Channel, the Mediterranean Sea, and the countries of Belgium, Germany, Italy, Spain, Switzerland, the principalities of Andorra and Monaco, and by the duchy of Luxembourg. Its capital is Paris.
Elements of residence that characterize a population. They are applicable in determining need for and utilization of health services.
Procedures for finding the mathematical function which best describes the relationship between a dependent variable and one or more independent variables. In linear regression (see LINEAR MODELS) the relationship is constrained to be a straight line and LEAST-SQUARES ANALYSIS is used to determine the best fit. In logistic regression (see LOGISTIC MODELS) the dependent variable is qualitative rather than continuously variable and LIKELIHOOD FUNCTIONS are used to find the best relationship. In multiple regression, the dependent variable is considered to depend on more than a single independent variable.
A range of values for a variable of interest, e.g., a rate, constructed so that this range has a specified probability of including the true value of the variable.
An infant during the first month after birth.
Research techniques that focus on study designs and data gathering methods in human and animal populations.
The number of new cases of a given disease during a given period in a specified population. It also is used for the rate at which new events occur in a defined population. It is differentiated from PREVALENCE, which refers to all cases, new or old, in the population at a given time.
Binary classification measures to assess test results. Sensitivity or recall rate is the proportion of true positives. Specificity is the probability of correctly determining the absence of a condition. (From Last, Dictionary of Epidemiology, 2d ed)
Studies in which individuals or populations are followed to assess the outcome of exposures, procedures, or effects of a characteristic, e.g., occurrence of disease.
Statistical models in which the value of a parameter for a given value of a factor is assumed to be equal to a + bx, where a and b are constants. The models predict a linear regression.
The statistical reproducibility of measurements (often in a clinical context), including the testing of instrumentation or techniques to obtain reproducible results. The concept includes reproducibility of physiological measurements, which may be used to develop rules to assess probability or prognosis, or response to a stimulus; reproducibility of occurrence of a condition; and reproducibility of experimental results.
A vital statistic measuring or recording the rate of death from any cause in hospitalized populations.
The presence of bacteria, viruses, and fungi in food and food products. This term is not restricted to pathogenic organisms: the presence of various non-pathogenic bacteria and fungi in cheeses and wines, for example, is included in this concept.
Levels within a diagnostic group which are established by various measurement criteria applied to the seriousness of a patient's disorder.
Educational attainment or level of education of individuals.
A prediction of the probable outcome of a disease based on a individual's condition and the usual course of the disease as seen in similar situations.
Individuals whose ancestral origins are in the continent of Europe.
A latent susceptibility to disease at the genetic level, which may be activated under certain conditions.
A country spanning from central Asia to the Pacific Ocean.
Functions constructed from a statistical model and a set of observed data which give the probability of that data for various values of the unknown model parameters. Those parameter values that maximize the probability are the maximum likelihood estimates of the parameters.
Evaluation undertaken to assess the results or consequences of management and procedures used in combating disease in order to determine the efficacy, effectiveness, safety, and practicability of these interventions in individual cases or series.
Application of statistical procedures to analyze specific observed or assumed facts from a particular study.
The presence of co-existing or additional diseases with reference to an initial diagnosis or with reference to the index condition that is the subject of study. Comorbidity may affect the ability of affected individuals to function and also their survival; it may be used as a prognostic indicator for length of hospital stay, cost factors, and outcome or survival.
The level of health of the individual, group, or population as subjectively assessed by the individual or by more objective measures.
A group of people with a common cultural heritage that sets them apart from others in a variety of social relationships.
The genetic constitution of the individual, comprising the ALLELES present at each GENETIC LOCUS.
Theoretical representations that simulate the behavior or activity of biological processes or diseases. For disease models in living animals, DISEASE MODELS, ANIMAL is available. Biological models include the use of mathematical equations, computers, and other electronic equipment.
Behaviors associated with the ingesting of alcoholic beverages, including social drinking.
The probability that an event will occur. It encompasses a variety of measures of the probability of a generally unfavorable outcome.
Theoretical representations that simulate the behavior or activity of systems, processes, or phenomena. They include the use of mathematical equations, computers, and other electronic equipment.
Persons living in the United States having origins in any of the black groups of Africa.
A distribution in which a variable is distributed like the sum of the squares of any given independent random variable, each of which has a normal distribution with mean of zero and variance of one. The chi-square test is a statistical test based on comparison of a test statistic to a chi-square distribution. The oldest of these tests are used to detect whether two or more population distributions differ from one another.
An indicator of body density as determined by the relationship of BODY WEIGHT to BODY HEIGHT. BMI=weight (kg)/height squared (m2). BMI correlates with body fat (ADIPOSE TISSUE). Their relationship varies with age and gender. For adults, BMI falls into these categories: below 18.5 (underweight); 18.5-24.9 (normal); 25.0-29.9 (overweight); 30.0 and above (obese). (National Center for Health Statistics, Centers for Disease Control and Prevention)
A status with BODY WEIGHT that is grossly above the acceptable or desirable weight, usually due to accumulation of excess FATS in the body. The standards may vary with age, sex, genetic or cultural background. In the BODY MASS INDEX, a BMI greater than 30.0 kg/m2 is considered obese, and a BMI greater than 40.0 kg/m2 is considered morbidly obese (MORBID OBESITY).
The exposure to potentially harmful chemical, physical, or biological agents that occurs as a result of one's occupation.
The science and art of collecting, summarizing, and analyzing data that are subject to random variation. The term is also applied to the data themselves and to the summarization of the data.
Typical way of life or manner of living characteristic of an individual or group. (From APA, Thesaurus of Psychological Index Terms, 8th ed)
Systematic gathering of data for a particular purpose from various sources, including questionnaires, interviews, observation, existing records, and electronic devices. The process is usually preliminary to statistical analysis of the data.
A procedure consisting of a sequence of algebraic formulas and/or logical steps to calculate or determine a given task.
A single nucleotide variation in a genetic sequence that occurs at appreciable frequency in the population.
Includes the spectrum of human immunodeficiency virus infections that range from asymptomatic seropositivity, thru AIDS-related complex (ARC), to acquired immunodeficiency syndrome (AIDS).
Tumors or cancer of the human BREAST.
Persistently high systemic arterial BLOOD PRESSURE. Based on multiple readings (BLOOD PRESSURE DETERMINATION), hypertension is currently defined as when SYSTOLIC PRESSURE is consistently greater than 140 mm Hg or when DIASTOLIC PRESSURE is consistently 90 mm Hg or more.
Measurable and quantifiable biological parameters (e.g., specific enzyme concentration, specific hormone concentration, specific gene phenotype distribution in a population, presence of biological substances) which serve as indices for health- and physiology-related assessments, such as disease risk, psychiatric disorders, environmental exposure and its effects, disease diagnosis, metabolic processes, substance abuse, pregnancy, cell line development, epidemiologic studies, etc.
The regular and simultaneous occurrence in a single interbreeding population of two or more discontinuous genotypes. The concept includes differences in genotypes ranging in size from a single nucleotide site (POLYMORPHISM, SINGLE NUCLEOTIDE) to large nucleotide sequences visible at a chromosomal level.
Theoretical representations that simulate the behavior or activity of genetic processes or phenomena. They include the use of mathematical equations, computers, and other electronic equipment.
A class of statistical procedures for estimating the survival function (function of time, starting with a population 100% well at a given time and providing the percentage of the population still well at later times). The survival analysis is then used for making inferences about the effects of treatments, prognostic factors, exposures, and other covariates on the function.
A statistical technique that isolates and assesses the contributions of categorical independent variables to variation in the mean of a continuous dependent variable.
Computer-based representation of physical systems and phenomena such as chemical processes.

Analysis of the effect of conversion from open to closed surgical intensive care unit. (1/24374)

OBJECTIVE: To compare the effect on clinical outcome of changing a surgical intensive care unit from an open to a closed unit. DESIGN: The study was carried out at a surgical intensive care unit in a large tertiary care hospital, which was changed on January 1, 1996, from an open unit, where private attending physicians contributed and controlled the care of their patients, to a closed unit, where patients' medical care was provided only by the surgical critical care team (ABS or ABA board-certified intensivists). A retrospective review was undertaken over 6 consecutive months in each system, encompassing 274 patients (125 in the open-unit period, 149 in the closed-unit period). Morbidity and mortality were compared between the two periods, along with length-of-stay (LOS) and number of consults obtained. A set of independent variables was also evaluated, including age, gender, APACHE III scores, the presence of preexisting medical conditions, the use of invasive monitoring (Swan-Ganz catheters, central and arterial lines), and the use of antibiotics, low-dose dopamine (LDD) for renal protection, vasopressors, TPN, and enteral feeding. RESULTS: Mortality (14.4% vs. 6.04%, p = 0.012) and the overall complication rate (55.84% vs. 44.14%, p = 0.002) were higher in the open-unit group versus the closed-unit group, respectively. The number of consults obtained was decreased (0.6 vs. 0.4 per patient, p = 0.036), and the rate of occurrence of renal failure was higher in the open-unit group (12.8% vs. 2.67%, p = 0.001). The mean age of the patients was similar in both groups (66.48 years vs. 66.40, p = 0.96). APACHE III scores were slightly higher in the open-unit group but did not reach statistical significance (39.02 vs. 36.16, p = 0.222). There were more men in the first group (63.2% vs. 51.3%). The use of Swan-Ganz catheters or central and arterial lines were identical, as was the use of antibiotics, TPN, and enteral feedings. The use of LDD was higher in the first group, but the LOS was identical. CONCLUSIONS: Conversion of a tertiary care surgical intensive care unit from an open to closed environment reduced dopamine usage and overall complication and mortality rates. These results support the concept that, when possible, patients in surgical intensive care units should be managed by board-certified intensivists in a closed environment.  (+info)

Antiphospholipid, anti-beta 2-glycoprotein-I and anti-oxidized-low-density-lipoprotein antibodies in antiphospholipid syndrome. (2/24374)

Antiphospholipid antibodies (aPL), anti-beta 2-glycoprotein I (anti-beta 2-GPI) and anti-oxidized-low-density lipoprotein (LDL) antibodies are all implicated in the pathogenesis of antiphospholipid syndrome. To investigate whether different autoantibodies or combinations thereof produced distinct effects related to their antigenic specificities, we examined the frequencies of antiphospholipid syndrome (APS)-related features in the presence of different antibodies [aPL, beta 2-GPI, anti-oxidized low density lipoprotein (LDL)] in 125 patients with APS. Median follow-up was 72 months: 58 patients were diagnosed as primary APS and 67 as APS plus systemic lupus erythematosus (SLE). Anticardiolipin antibodies (aCL), anti-beta 2-GPI and anti-oxidized LDL antibodies were determined by ELISA; lupus anticoagulant (LA) by standard coagulometric methods. Univariate analysis showed that patients positive for anti-beta 2-GPI had a higher risk of recurrent thrombotic events (OR = 3.64, 95% CI, p = 0.01) and pregnancy loss (OR = 2.99, 95% CI, p = 0.004). Patients positive for anti-oxidized LDL antibodies had a 2.24-fold increase in the risk of arterial thrombosis (2.24, 95% CI, p = 0.03) and lower risk of thrombocytopenia (OR = 0.41 95% CI, p = 0.04). Patients positive for aCL antibodies had a higher risk of pregnancy loss (OR = 4.62 95% CI, p = 0.001). When these data were tested by multivariate logistic regression, the association between anti-beta 2-GPI and pregnancy loss and the negative association between anti-oxidized LDL antibodies and thrombocytopenia disappeared.  (+info)

Capture-recapture models including covariate effects. (3/24374)

Capture-recapture methods are used to estimate the incidence of a disease, using a multiple-source registry. Usually, log-linear methods are used to estimate population size, assuming that not all sources of notification are dependent. Where there are categorical covariates, a stratified analysis can be performed. The multinomial logit model has occasionally been used. In this paper, the authors compare log-linear and logit models with and without covariates, and use simulated data to compare estimates from different models. The crude estimate of population size is biased when the sources are not independent. Analyses adjusting for covariates produce less biased estimates. In the absence of covariates, or where all covariates are categorical, the log-linear model and the logit model are equivalent. The log-linear model cannot include continuous variables. To minimize potential bias in estimating incidence, covariates should be included in the design and analysis of multiple-source disease registries.  (+info)

Risk factors for injuries and other health problems sustained in a marathon. (4/24374)

OBJECTIVES: To identify risk factors for injuries and other health problems occurring during or immediately after participation in a marathon. METHODS: A prospective cohort study was undertaken of participants in the 1993 Auckland Citibank marathon. Demographic data, information on running experience, training and injuries, and information on other lifestyle factors were obtained from participants before the race using an interviewer-administered questionnaire. Information on injuries and other health problems sustained during or immediately after the marathon were obtained by a self administered questionnaire. Logistic regression analyses were undertaken to identify significant risk factors for health problems. RESULTS: This study, one of only a few controlled epidemiological studies that have been undertaken of running injuries, has identified a number of risk factors for injuries and other health problems sustained in a marathon. Men were at increased risk of hamstring and calf problems, whereas women were at increased risk of hip problems. Participation in a marathon for the first time, participation in other sports, illness in the two weeks before the marathon, current use of medication, and drinking alcohol once a month or more, were associated with increased self reported risks of problems. While increased training seemed to increase the risk of front thigh and hamstring problems, it may decrease the risk of knee problems. There are significant but complex relations between age and risk of injury or health problem. CONCLUSIONS: This study has identified certain high risk subjects and risk factors for injuries and other health problems sustained in a marathon. In particular, subjects who have recently been unwell or are taking medication should weigh up carefully the pros and cons of participating.  (+info)

Early mycological treatment failure in AIDS-associated cryptococcal meningitis. (5/24374)

Cryptococcal meningitis causes significant morbidity and mortality in persons with AIDS. Of 236 AIDS patients treated with amphotericin B plus flucytosine, 29 (12%) died within 2 weeks and 62 (26%) died before 10 weeks. Just 129 (55%) of 236 patients were alive with negative cerebrospinal fluid (CSF) cultures at 10 weeks. Multivariate analyses identified that titer of cryptococcal antigen in CSF, serum albumin level, and CD4 cell count, together with dose of amphotericin B, had the strongest joint association with failure to achieve negative CSF cultures by day 14. Among patients with similar CSF cryptococcal antigen titers, CD4 cell counts, and serum albumin levels, the odds of failure at week 10 for those without negative CSF cultures by day 14 was five times that for those with negative CSF cultures by day 14 (odds ratio, 5.0; 95% confidence interval, 2.2-10.9). Prognosis is dismal for patients with AIDS-related cryptococcal meningitis. Multivariate analyses identified three components that, along with initial treatment, have the strongest joint association with early outcome. Clearly, more effective initial therapy and patient management strategies that address immune function and nutritional status are needed to improve outcomes of this disease.  (+info)

The Sock Test for evaluating activity limitation in patients with musculoskeletal pain. (6/24374)

BACKGROUND AND PURPOSE: Assessment within rehabilitation often must reflect patients' perceived functional problems and provide information on whether these problems are caused by impairments of the musculoskeletal system. Such capabilities were examined in a new functional test, the Sock Test, simulating the activity of putting on a sock. SUBJECTS AND METHODS: Intertester reliability was examined in 21 patients. Concurrent validity, responsiveness, and predictive validity were examined in a sample of 337 patients and in subgroups of this sample. RESULTS: Intertester reliability was acceptable. Sock Test scores were related to concurrent reports of activity limitation in dressing activities. Scores also reflected questionnaire-derived reports of problems in a broad range of activities of daily living and pain and were responsive to change over time. Increases in age and body mass index increased the likelihood of Sock Test scores indicating activity limitation. Pretest scores were predictive of perceived difficulties in dressing activities after 1 year. CONCLUSION AND DISCUSSION: Sock Test scores reflect perceived activity limitations and restrictions of the musculoskeletal system.  (+info)

Modified cuspal relationships of mandibular molar teeth in children with Down's syndrome. (7/24374)

A total of 50 permanent mandibular 1st molars of 26 children with Down's syndrome (DS) were examined from dental casts and 59 permanent mandibular 1st molars of normal children were examined from 33 individuals. The following measurements were performed on both right and left molars (teeth 46 and 36 respectively): (a) the intercusp distances (mb-db, mb-d, mb-dl, db-ml, db-d, db-dl, db-ml, d-dl, d-ml, dl-ml); (b) the db-mb-ml, mb-db-ml, mb-ml-db, d-mb-dl, mb-d-dl, mb-dl-d angles; (c) the area of the pentagon formed by connecting the cusp tips. All intercusp distances were significantly smaller in the DS group. Stepwise logistic regression, applied to all the intercusp distances, was used to design a multivariate probability model for DS and normals. A model based on 2 distances only, mb-dl and mb-db, proved sufficient to discriminate between the teeth of DS and the normal population. The model for tooth 36 for example was as follows: p(DS) = (e(30.6-5.6(mb-dl)+25(mb-db)))/(1 + e(30.6 5.6(mb-dl)+25(mb db))). A similar model for tooth 46 was also created, as well as a model which incorporated both teeth. With respect to the angles, significant differences between DS and normals were found in 3 out of the 6 angles which were measured: the d-mb-dl angle was smaller than in normals, the mb-d-dl angle was higher, and the mb-dl-d angle was smaller. The dl cusp was located closer to the centre of the tooth. The change in size occurs at an early stage, while the change in shape occurs in a later stage of tooth formation in the DS population.  (+info)

Organizational and environmental factors associated with nursing home participation in managed care. (8/24374)

OBJECTIVE: To develop and test a model, based on resource dependence theory, that identifies the organizational and environmental characteristics associated with nursing home participation in managed care. DATA SOURCES AND STUDY SETTING: Data for statistical analysis derived from a survey of Directors of Nursing in a sample of nursing homes in eight states (n = 308). These data were merged with data from the On-line Survey Certification and Reporting System, the Medicare Managed Care State/County Data File, and the 1995 Area Resource File. STUDY DESIGN: Since the dependent variable is dichotomous, the logistic procedure was used to fit the regression. The analysis was weighted using SUDAAN. FINDINGS: Participation in a provider network, higher proportions of resident care covered by Medicare, providing IV therapy, greater availability of RNs and physical therapists, and Medicare HMO market penetration are associated with a greater likelihood of having a managed care contract. CONCLUSION: As more Medicare recipients enroll in HMOs, nursing home involvement in managed care is likely to increase. Interorganizational linkages enhance the likelihood of managed care participation. Nursing homes interested in managed care should consider upgrading staffing and providing at least some subacute services.  (+info)

You predict a class if it is given maximum probability by the estimated model. So when some classes are not predicted that is simply because the model never gave them maximum probability. That might just be correct, and not necessary a reason for concern. So I disagree with the answer by @Nitin, proposing oversampling.. You might say: but they did occur in the data. Yes, but it might have been rare occurrences! never really the most probable outcome given the predictors in the model. You didnt give us a context, what your classes represent in the real world. You have very unbalanced classes. That might be because some classes really are uncommon in your population, or it might be some problems with data collection. You didnt tell us. But it is difficult to see that over (or under)-sampling can achieve anything that cannot be achieved using weights. Even more important, you are using (ordinal) logistic regression, which is not a classifier, see Why isnt Logistic Regression called Logistic ...
Background: Knowledge of utilization of health services and asso- ciated factors is important in planning and delivery of interventions to improve health services coverage. This knowledge is however limited in many developing countries. We determined the preva- lence and factors associated with health services utilization in a rural area of Kenya. Our findings inform the local health management in development of appropriately targeted interventions. Methods: Design: Cluster sample survey. Population: Residents of Kaloleni sub-County in Kenya. Participants/respondents: Household key informants. Outcomes: (i) History of illness for household members and (ii) health services utilization in the preceding month, (iii) factors associated with health services utilization. Analyses: Estimation of prevalence (outcomes i and ii) and random effects logistic regression (outcome iii). Findings: 1230/6,440 (19.1%, 95% CI: 18.3%-20.2%) household members reported an illness in the month preceding the survey. Of these,
and other continuous variables), the interpretation is that when a students gpa moves 1 unit, the odds of moving from unlikely applying to somewhat likely or very likley applying (or from the lower and middle categories to the high category) are multiplied by 1.85.. One of the assumptions underlying ordinal logistic (and ordinal probit) regression is that the relationship between each pair of outcome groups is the same. In other words, ordinal logistic regression assumes that the coefficients that describe the relationship between, say, the lowest versus all higher categories of the response variable are the same as those that describe the relationship between the next lowest category and all higher categories, etc. This is called the proportional odds assumption or the parallel regression assumption. Because the relationship between all pairs of groups is the same, there is only one set of coefficients. If this was not the case, we would need different sets of coefficients in the model ...
Logistic regression model is a branch of the generalized linear models and is widely used in many areas of scientific research. The logit link function and the binary dependent variable of interest make the logistic regression model distinct from linear regression model. The conclusion drawn from a fitted logistic regression model could be incorrect or misleading when the covariates can not explain and /or predict the response variable accurately based on the fitted model- that is, lack-of-fit is present in the fitted logistic regression model. The current goodness-of-fit tests can be roughly categorized into four types. (1) The tests are based on covariate patterns, e.g., Pearsons Chi-square test, Deviance D test, and Osius and Rojeks normal approximation test. (2) Hosmer-Lemeshows C and Hosmer-Lemeshows H tests are based on the estimated probabilities. (3) Score tests are based on the comparison of two models, where the assumed logistic regression model is embedded into a more general ...
Illustration:. FYI there are 4 variables: Variable 1 (independent, coded 0/1) Variable 2 (independent, coded 0/1) Variable 3 (independent, coded 0/1) & the Dependent (coded 0/1). Model 1 Logistic regression including direct effects of all independents on the dependent. Normal results. Model 2 Logistic regression including direct effects of all independents on the dependent AND the effects of 2 interaction terms on the dependent. The interaction terms are the product of variable 1 times variable 2 and the product of variable 1 times variable 3. Results include extremely high B and odds-ratio coefficients and standard error values. For example: B=-20.799, SE=40192.876, odds-ratio= 1153680239. *I have also found that if I do NOT include the direct effects of Variable 1 in Model 1 OR if I only include 1 interaction term in Model 2, the effects go back to normal.. Thank you so much!!!. ...
2012/2/16 Maria Niarchou : ,, I would like to perform a hierarchical logistic regression analysis in which ,, independent variables are entered in blocks. Hireg doesnt seem to work with categorical outcomes. ,, Could you please let me know if there is an alternative command to do this? -hireg- is a user written program, so per the Statalist FAQ you must tell us where you got it from. The purpose of that rule is not to make your life hard, but to make sure that all of us are talking about the same program. There are often different versions of user written programs floating around in cyber space, if you do not tell us which version you are referring to than it can easily happen that we are talking about different versions and you will get advise that does not help you. Anyhow, it is good news that -hireg- (I assume you got it from SSC) does not work with logistic regression, because that is not a good idea with non-linear models like -logit-. A lot of the nice properties of these comparisons ...
R package rms: Regression Modeling Strategies , Regression modeling, testing, estimation, validation, graphics, prediction, and typesetting by storing enhanced model design attributes in the fit. rms is a collection of 229 functions that assist with and streamline modeling. It also contains functions for binary and ordinal logistic regression models and the Buckley-James multiple regression model for right-censored responses, and implements penalized maximum likelihood estimation for logistic and ordinary linear models. rms works with almost any regression model, but it was especially written to work with binary or ordinal logistic regression, Cox regression, accelerated failure time models, ordinary linear models, the Buckley-James model, generalized least squares for serially or spatially correlated observations, generalized linear models, and quantile regression.
Hi, , , I would like to perform a hierarchical logistic regression analysis in which , independent variables are entered in blocks. Hireg doesnt seem to work with categorical outcomes. , Could you please let me know if there is an alternative command to do this? , , Thanks, , Maria * * For searches and help try: * * * ...
Hello! I am a psychologist working on data from a psychophysical experiment. I analyze the data in a hierarchical logistic regression with 7 predictors: three main predictors (manipulated in the experiment, within each…
You have many choices of performing logistic regression in the SAS System. The CATMOD, GENMOD, GLIMMIX, LOGISTIC, PROBIT, and SURVEYLOGISTIC procedures fit the usual logistic regression model. PROC CATMOD might not be efficient when there are continuous independent variables with large numbers of different values. For a continuous variable with a very limited number of values, PROC CATMOD might still be useful. PROC GLIMMIX enables you to specify random effects in the models; in particular, you can fit a random-intercept logistic regression model. PROC LOGISTIC provides the capability of model-building and performs conditional and exact conditional logistic regression. It can also use Firths bias-reducing penalized likelihood method. PROC PROBIT enables you to estimate the natural response rate and compute fiducial limits for the dose variable. The LOGISTIC, GENMOD, GLIMMIX, PROBIT, and SURVEYLOGISTIC procedures can analyze summarized data by enabling you to input the numbers of events and ...
TY - JOUR. T1 - Multivariate logistic regression for familial aggregation of two disorders. II. Analysis of studies of eating and mood disorders. AU - Hudson, James I.. AU - Laird, Nan M.. AU - Betensky, Rebecca A.. AU - Keck, Paul E.. AU - Pope, Harrison G.. PY - 2001/3/1. Y1 - 2001/3/1. N2 - Family studies have suggested that eating disorders and mood disorders may coaggregate within families. Previous studies, however, have been limited by use of univariate modeling techniques and failure to account for the correlation of observations within families. To provide a more efficient analysis and to illustrate multivariate logistic regression models for familial aggregation of two disorders, the authors analyzed pooled data from two previously published family studies (conducted in Massachusetts in 1984-1986 and 1986-1987) by using multivariate proband predictive and family predictive models. Both models demonstrated a significant familial aggregation of mood disorders and familial coaggregation ...
STUDY OBJECTIVE--To assess the extent to which the size of socioeconomic inequalities in self reported health varies among industrialised countries. DESIGN--Cross sectional data on the association between educational level and several health indicators were obtained from national health interview surveys. This association was quantified by means of an inequality index based on logistic regression analysis. SETTING--The national, non-institutionalised populations of the United Kingdom, Sweden, Denmark, Germany, The Netherlands, Italy, the United States, and Canada were studied. The age group was 15-64 years, and the study period was 1983-90. PARTICIPANTS--Representative population samples with the number of respondents ranging from approximately 6000 (Denmark) to 90,000 (the United States) were studied. MAIN RESULTS--For men, the smallest health inequalities were observed for the United Kingdom and Sweden, and the largest inequalities for Italy and the United States. Other countries held an ...
BACKGROUND: The greater participation of women in medicine in recent years, and recent trends showing that doctors of both sexes work fewer hours than in the past, present challenges for medical workforce planning. In this study, we provide a detailed analysis of the characteristics of doctors who choose to work less-than-full-time (LTFT). We aimed to determine the influence of these characteristics on the probability of working LTFT. METHODS: We used data on working patterns obtained from long-term surveys of 10,866 UK-trained doctors. We analysed working patterns at 10 years post-graduation for doctors of five graduating cohorts, 1993, 1996, 1999, 2000 and 2002 (i.e. in the years 2003, 2006, 2009, 2010 and 2012, respectively). We used multivariable binary logistic regression models to examine the influence of a number of personal and professional characteristics on the likelihood of working LTFT in male and female doctors. RESULTS: Across all cohorts, 42 % of women and 7 % of men worked LTFT. For
AIMS: The aim of this study was to evaluate the association of diabetes and diabetes treatment with risk of postmenopausal breast cancer. METHODS: Histologically confirmed incident cases of postmenopausal breast (N = 916) cancer were recruited from 23 Spanish public hospitals. Population-based controls (N = 1094) were randomly selected from primary care center lists within the catchment areas of the participant hospitals. ORs (95 % CI) were estimated using mixed-effects logistic regression models, using the recruitment center as a random effect term. Breast tumors were classified into hormone receptor positive (ER+ or PR+), HER2+ and triple negative (TN). RESULTS: Diabetes was not associated with the overall risk of breast cancer (OR 1.09; 95 % CI 0.82-1.45), and it was only linked to the risk of developing TN tumors: Among 91 women with TN tumors, 18.7 % were diabetic, while the corresponding figure among controls was 9.9 % (OR 2.25; 95 % CI 1.22-4.15). Regarding treatment, results showed that ...
Linifanib organ failing within the model whatever the univariate evaluation, we required ~70 occasions. In retrospect, simply 5 indie predictors of complete conformity had been place and discovered in the ultimate model, so ~50 occasions were needed, significantly less than the 77 occasions in todays research. To explore the comparative influence of quality of ED treatment (as quantified by the amount of SSC targets attained), illness intensity (as quantified with the PIRO rating [17]), and disposition towards the ICU or ward on mortality inside our research cohort with fairly low mortality, we place these three variables within a binary logistic regression model with in-hospital mortality as an final result measure similarly as defined above. We portrayed the consequences of predictor factors on conformity and medical center mortality using chances ratios (ORs) including 95% self-confidence intervals (CIs). Finally, because time and energy to antibiotics can be an essential predictor of ...
In this last article of a two-part series, learn more about Logistic Regression and how to build a logistic regression model with Log odds.
Customer due diligence begins with verifying each customers identity and assessing the associated risk. Assessing customer risk is an essential component of a comprehensive Bank Secrecy Act/Anti-Money Laundering (BSA/AML) monitoring program.. To meet risk governance regulatory expectations and accurately assess higher-risk customers, financial institutions are modernizing their customer risk rating models and moving their heuristic, rule-based customer risk rating models to statistical models, specifically ordinal logistic regression models.. These statistical models perform better than rules-based models, are easier to justify to the regulators and are easier to update, validate and maintain because they use an established and understood framework. They are quickly becoming standard due to the regulatory pressure to use more scientific approaches. ...
Agresti, Repeat Exercise 8.10 using an ordinal logistic regression model for job satisfaction as the outcome variable. Compare these results to the analysis in the previous problem ...
We used a case-control study (596 adults with specific moderate-complex CHDs and 2384 controls). We extracted age, race/ethnicity, electrocardiogram (EKG), and blood tests from routine outpatient visits (1/ 2009 through 12/2012). We used multivariable logistic regression models and a split-sample (4: 1 ratio) approach to develop and internally validate the risk score, respectively. We generated receiver operating characteristic (ROC) c-statistics and Brier scores to assess the ability of models to predict the presence of specific moderate-complex CHDs ...
Primary Care physicians were usually involved in MO process; with 33 cases; (55.9% of the population). Other medical specialties involved in the process were: Internal Medicine 3 (5%); Emergency 7 (11.8%); General Surgery 1 (6%) Digestive 6 (10.1%); and others 9 (15.2%).. The waiting times are shown in table V. The waiting time between the first physician-patient meeting and the date when the patient was referred to the consultant, was the longest one in every cases. Having at least one MO, represented an average of 235.8 days of delay compared to 8.7 days in the group that had no MO.. Delays presented in table VI were classified according to the symptoms. Iron deficiency anemia was the main clinical key related to the increasing delay, which had an average of 300 days in the first time range.. We used multivariable logistic regression model. Only the number of co-morbid medical diseases was associated with the presence of MO when we controlled gender and age (OR: 1.66; CI 95% 1.8-2.35; p = ...
This section describes the dialog box tabs that are associated with the Logistic Regression analysis. The Logistic Regression analysis calls the LOGISTIC procedure in SAS/STAT software. See the LOGISTIC documentation in the SAS/STAT Users Guide for details. ...
Hi all! I am trying to develop a plot a figure in which I would like to show the odds ratios obtained from a logistic model. I have tried with the dotplot option but no success. Could you help me? Is there any option when modelling the logistic model in R? Thank you in advance ...
We used longitudinal data from the FinnTwin12-17 study with baseline at age 11-12 and follow-up at ages 14 and 17(1/2), including 4138 individuals. The outcome was self-reported ever use of cannabis or other illicit drugs at age 17(1/2). The potential predictors were measures reported by the twins, their parents or teachers. As individual factors we tested smoking, alcohol use, behavioral and emotional problems; as peer factors: number of smoking friends and acquaintances with drug experience; as family factors: parental substance use, socio-economic status and pre-natal exposure to nicotine. We used logistic regression models, controlling for twinship, age and sex, to compute odds ratios (OR) for each potential predictor. To adjust for within-family confounds, we conducted conditional logistic regressions among 246 twin pairs discordant for drug use ...
View Notes - Ch5-4 from ST 3241 at Adams State University. Outline 5.1 Interpreting Parameters in Logistic Regression Chapter 5. Logistic Regression 5.2 Inference for Logistic Regression Deyuan Li
Could I enter all my independent variables directly for a binary logistic regression OR is it necessary that I should run a one way anova for each of...
Human Resource Practices as Predictors of Work-Family Conflict and Employee Engagement among Employees in Indian Insurance Companies: An Application of Multinomial Logistic Regression Analysis. Authored. By. Dr. Sudhir Chandra Das. Professor of OB & HR. Faculty of Commerce. Banaras Hindu University. Varanasi-5, UP State. India. E-Mail: [email protected] Cell: +91- 9415624673. Tel: +91- 0542-2575367. ABSTRACT. Aim of the Study: The study is intended to understand the influences of perceived HR practices of globalised Indian insurance companies on work family conflict and employee engagement.. Research Philosophy and Strategy: Underlying principle of the study is deterministic philosophy based whereas the paper highlights established causes with outcomes. The study applied Multinomial Logistic Regression (MLR) model which is one of the important methods for categorical data analysis experimented with ten well practiced HR issues namely job design, flexi-schedule, working conditions, performance ...
Logistic Regression courses from top universities and industry leaders. Learn Logistic Regression online with courses like Regression Models and Logistic Regression in R for Public Health.
Approximate confidence intervals are given for the odds ratios derived from the covariates.. Bootstrap estimates A bootstrap procedure may be used to cross-validate confidence intervals calculated for odds ratios derived from fitted logistic models (Efron and Tibshirani, 1997; Gong, 1986). The bootstrap confidence intervals used here are the bias-corrected type.. The mechanism that StatsDirect uses is to draw a specified number of random samples (with replacement, i.e. some observations are drawn once only, others more than once and some not at all) from your data. These re-samples are fed back into the logistic regression and bootstrap estimates of confidence intervals for the model parameters are made by examining the model parameters calculated at each cycle of the process. The bias statistic shows how much each mean model parameter from the bootstrap distribution deviates from observed model parameters.. Classification and ROC curve The confidence interval given with the likelihood ...
Linear and logistic regression models Stats Linear and logistic regression models Assignment Help Linear regression makes use of the basic linear formula Y= b0+ ∑( biXi)+ ϵY= b0+ ∑( biXi)+ ϵ where YY
Here is an example of Fit a logistic regression model: Once you have your random training and test sets you can fit a logistic regression model to your training set using the glm() function.
This paper develops a Two-echelon logistics model for recoverable items with lateral supply in a single period in the Army. It derives formulas for distribution and inventory costs, and determines the optimal stock level at a depot and at a base to minimize the backorders. During the period, a Poisson demand and distinct units of operating bases which stocks can be shared are assumed. It allows the lateral supply between operating bases. A two-phase method is used to get solutions to the stock level constrained optimization problem. The outcome of this paper can be applied to lower level of logistics units (e.g. Division, Regiment) rather than higher level of logistics units of the Army (e.g. Corps, Theater) which distribute recoverable items periodically and repair the defective items transited from subordinate units.Dept. of Industrial and Manufacturing Systems Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis1997 .C42. Source: Masters
Can I use SPSS MIXED models for (a) ordinal logistic regression, and (b) multi-nomial logistic regression? Every once in a while I get emailed a question that I think others will find helpful. This is definitely one of them. My
Stata provides two commands for logistic regression: logit and logistic. Logit reports coefficients; whereas logistic reports odds ratios. The general command for logistic regression appears like this ...
A Panel on Logistics in Rail Systems was Held in KBU - A Panel on Logistics in Rail Systems was Held in KBU: Rail Systems Engineering Club of Karabuk University (KBU)
Solved: Please allow me to give a discussion on the nomial logit models and ordinal logit models. I ran analysis for a data using both methods. But
Logistic Regression Tutorial Stanford University - logistic regression. Learn more about logistic regression, regression, singular to working precision, nan
The Interport: A Logistics Model and an Application to the Distribution of Maritime Containers: 10.4018/jisscm.2012100102: In a container transportation and logistics network, an interport is a common user facility located in the hinterland of one or several seaports where
The Sonar data set is loaded using the Retrieve operator. The Split Validation operator is applied on it for training and testing a regression model. The Logistic Regression (Evolutionary) operator is applied in the training subprocess of the Split Validation operator. All parameters are used with default values. The Logistic Regression (Evolutionary) operator generates a regression model. The Apply Model operator is used in the testing subprocess to apply this model on the testing data set. The resultant labeled ExampleSet is used by the Performance operator for measuring the performance of the model. The regression model and its performance vector are connected to the output and it can be seen in the Results Workspace. ...
Modeling conditional probabilities; using regression to model probabilities; transforming probabilities to work better with regression; the logistic regression model; maximum likelihood; numerical maximum likelihood by Newtons method and by iteratively re-weighted least squares; comparing logistic regression to logistic-additive models. Reading: Notes, chapter 12 ...
Modeling conditional probabilities; using regression to model probabilities; transforming probabilities to work better with regression; the logistic regression model; maximum likelihood; numerical maximum likelihood by Newtons method and by iteratively re-weighted least squares; comparing logistic regression to logistic-additive models. Reading: Notes, chapter 12 ...
Linear logistic models with relaxed assumptions (LLRA) are a flexible tool for item-based measurement of change or multidimensional Rasch models. Their key features are to allow for multidimensional...
White Paper with an in-depth analysis of the contrasts between Traditional and eCommerce Logistics Models and how the shift is affecting the former.
Nafiah Aprilia1), Didik Tamtomo2), Endang Sutisna Sulaeman2) 1)Masters Program in Public Health, Universitas Sebelas Maret 2)Faculty of
This video shows how to create, train, save, and deploy a logistic regression model that assesses the likelihood that a customer of an outdoor equipment company will buy a tent based on age, sex, marital status and job profession. After watching the video, try the step-by-step tutorial.. ...
Trains logistic regression model by discretizing continuous variables via gradient boosting approach. The proposed method tries to achieve a tradeoff between interpretation and prediction accuracy for logistic regression by discretizing the continuous variables. The variable binning is accomplished in a supervised fashion. The model trained by this package is still a single logistic regression model, but not a sequence of logistic regression models. The fitted model object returned from the model training consists of two tables. One table is used to give the boundaries of bins for each continuous variable as well as the corresponding coefficients, and the other one is used for discrete variables. This package can also be used for binning continuous variables for other statistical analysis. ...
TY - JOUR. T1 - Effects of different type of covariates and sample size on parameter estimation for multinomial logistic regression model. AU - Hamid, Hamzah Abdul. AU - Wah, Yap Bee. AU - Xie, Xian Jin. PY - 2016. Y1 - 2016. N2 - The sample size and distributions of covariate may affect many statistical modeling techniques. This paper investigates the effects of sample size and data distribution on parameter estimates for multinomial logistic regression. A simulation study was conducted for different distributions (symmetric normal, positively skewed, negatively skewed) for the continuous covariates. In addition, we simulate categorical covariates to investigate their effects on parameter estimation for the multinomial logistic regression model. The simulation results show that the effect of skewed and categorical covariate reduces as sample size increases. The parameter estimates for normal distribution covariate apparently are less affected by sample size. For multinomial logistic regression ...
Purpose: : To determine whether birth weight (BW), Z-score, or centile gives a better prediction of the risk of requiring treatment in a logistic regression model of retinopathy of prematurity (ROP) and to test for higher order relationships in these covariates. Methods: : A retrospective study of 299 infants of 32 weeks Gestational Age (GA) or less who were screened for ROP at City Hospital, Birmingham, United Kingdom between 1 January 2001 and 31 October 2009. A stepwise logistic regression model was used to examine the relative merits of GA, BW, Z-score and centile as predictors of the risk of requiring treatment for ROP (termed severe ROP). Then, three logistic regression models were compared: Model 1: GA and BW; Model 2: GA and Z-score; Model 3: GA and centile. Higher order relationships were explored using general linear model (GLM) analysis. Results: : Stepwise logistic regression chose GA and BW as the best predictors of risk of severe ROP. All models were statistically significant ...
Oral cancer is the most common cancer among Indian men, and has strong tendency of metastatic spread to neck lymph node which strongly influences prognosis especially 5 year survival-rate and also guides the related managements more effectively. Therefore, a reliable and accurate means of preoperative evaluation of extent of nodal involvement becomes crucial. However, earlier researchers have preferred to address mainly its dichotomous form (involved/not-involved) instead of ordinal form while dealing with epidemiology of nodal involvement. As a matter of fact, consideration of ordinal form appropriately may increase not only the efficiency of the developed model but also accuracy in the results and related implications. Hence, to develop a model describing factors associated with ordinal form of nodal involvement was major focus of this study. The data for model building were taken from the Department of Surgical Oncology, Dr.BRA-IRCH, AIIMS, New Delhi, India. All the OSCC patients (duly operated
When study data are clustered, standard regression analysis is considered inappropriate and analytical techniques for clustered data need to be used. For prediction research in which the interest of predictor effects is on the patient level, random effect regression models are probably preferred over standard regression analysis. It is well known that the random effect parameter estimates and the standard logistic regression parameter estimates are different. Here, we compared random effect and standard logistic regression models for their ability to provide accurate predictions. Using an empirical study on 1642 surgical patients at risk of postoperative nausea and vomiting, who were treated by one of 19 anesthesiologists (clusters), we developed prognostic models either with standard or random intercept logistic regression. External validity of these models was assessed in new patients from other anesthesiologists. We supported our results with simulation studies using intra-class correlation
TY - JOUR. T1 - Risk of postoperative infection after liver transplantation. T2 - A univariate and stepwise logistic regression analysis of risk factors in 150 consecutive patients. AU - Mora, N. P.. AU - Gonwa, T. A.. AU - Goldstein, R. M.. AU - Husberg, B. S.. AU - Klintmalm, G. B.. PY - 1992. Y1 - 1992. N2 - Between March 1988 and December 1989, 180 orthotopic liver transplants were performed in 150 adult patients. We have retrospectively reviewed all charts to determine incidence, timing and etiology of major postoperative infections. Major postoperative infection occurred during the 90-day period following transplantation in 47% of liver transplant patients. Bacterial infections dominated early, while viral and protozoal infections presented later. The most common organisms were Staphylococcus, Enterococcus, and cytomegalovirus. Significant variables associated with infection included pretransplant status, preoperative renal dysfunction, rejection, OKT3 therapy, postoperative renal and ...
The team assessed 541 patients of whom 85 had diabetes mellitus.. The median age at inclusion was 50 years.. The prevalence of diabetes mellitus was 11% for patients with Ishak fibrosis score 4, 13% for Ishak score 5, and 19% for Ishak score 6.. The team used multiple logistic regression analysis to show an increased risk of diabetes mellitus for patients with an elevated body mass index.. The research team noted a decreased risk of diabetes mellitus for patients with higher serum albumin levels.. The researchers found that during a median follow-up of 4 years, 13% of patients with diabetes mellitus versus 6% of patients without diabetes mellitus developed hepatocellular carcinoma.. The 5-year occurrence of hepatocellular carcinoma was 11% and 5%, respectively.. The team found that in patients with Ishak 6 cirrhosis, diabetes mellitus was independently associated with the development of hepatocellular carcinoma.. Dr Veldts team concluded, For patients with chronic Hepatitis C and advanced ...
Underreporting of childhood sexual abuse is a major barrier to obtaining reliable prevalence estimates. We tested the sensitivity and specificity of the face-to-face-interview (FTFI) method by comparing the number of disclosures of forced sex against a more confidential mode of data collection, the sealed-envelope method (SEM). We also report on characteristics of individuals associated with non-disclosure in FTFIs. Secondary analysis of data from a cross-sectional survey conducted in 2014, with n = 3843 children attending primary school in Luwero District, Uganda. Sensitivity and specificity were calculated, and mixed effects logistic regression models tested factors associated with disclosure in one or both modes. In the FTFI, 1.1% (n = 42) of children reported ever experiencing forced sex, compared to 7.0% (n = 268) in the SEM. The FTFI method demonstrated low sensitivity (13.1%, 95%CI 9.3-17.7%) and high specificity (99.8%, 95%CI 99.6-99.9%) in detecting cases of forced sex, when compared to the SEM
Underreporting of childhood sexual abuse is a major barrier to obtaining reliable prevalence estimates. We tested the sensitivity and specificity of the face-to-face-interview (FTFI) method by comparing the number of disclosures of forced sex against a more confidential mode of data collection, the sealed-envelope method (SEM). We also report on characteristics of individuals associated with non-disclosure in FTFIs. Secondary analysis of data from a cross-sectional survey conducted in 2014, with n = 3843 children attending primary school in Luwero District, Uganda. Sensitivity and specificity were calculated, and mixed effects logistic regression models tested factors associated with disclosure in one or both modes. In the FTFI, 1.1% (n = 42) of children reported ever experiencing forced sex, compared to 7.0% (n = 268) in the SEM. The FTFI method demonstrated low sensitivity (13.1%, 95%CI 9.3-17.7%) and high specificity (99.8%, 95%CI 99.6-99.9%) in detecting cases of forced sex, when compared to ...
Abstract. Respiratory damage is a main manifestation of severe Enterovirus 71 (EV71) infection. Polymorphisms of -403G/A (rs2107538), -28C/G (rs2280788), and In1.1T/C (rs2280789) in chemotactic chemokine ligand 5 (CCL5) have linked with many respiratory diseases. In this study, we explored the possible correlation of CCL5 polymorphisms with severe EV71 infection. Blood samples were obtained from 87 children hospitalized for EV71 infection. Fifty-seven healthy children were enrolled as asymptomatic controls. Genotype and allele frequencies were analyzed by logistic regression analysis. There were statistically significant differences in polymorphisms of CCL5 -403G/A and In1.1T/C for dominant model (P = 0.016; P = 0.027) and additive model (P = 0.010; P = 0.019) between patients with severe EV71 infection and asymptomatic controls. With ordinal logistic regression model analysis, statistically significant differences were found between polymorphisms of CCL5 (-403G/A) (P = 0.034) with the severity of EV71
High levels of social capital and social integration are associated with self-rated health in many developed countries. However, it is not known whether this association extends to non-western and less economically advanced countries. We examine associations between social support, volunteering, and self-rated health in 139 low-, middle- and high-income countries. Data come from the Gallup World Poll, an internationally comparable survey conducted yearly from 2005 to 2009 for those 15 and over. Volunteering was measured by self-reports of volunteering to an organization in the past month. Social support was based on self-reports of access to support from relatives and friends. We started by estimating random coefficient (multi-level) models and then used multivariate logistic regression to model health as a function of social support and volunteering, controlling for age, gender, education, marital status, and religiosity. We found statistically significant evidence of cross-national variation ...
PURPOSE: As global initiatives increase patient access to surgical treatments, there is a need to define optimal levels of perioperative care. Our aim was to describe the relationship between the provision and use of critical care resources and postoperative mortality. METHODS: Planned analysis of data collected during an international 7-day cohort study of adults undergoing elective in-patient surgery. We used risk-adjusted mixed-effects logistic regression models to evaluate the association between admission to critical care immediately after surgery and in-hospital mortality. We evaluated hospital-level associations between mortality and critical care admission immediately after surgery, critical care admission to treat life-threatening complications, and hospital provision of critical care beds. We evaluated the effect of national income using interaction tests. RESULTS: 44,814 patients from 474 hospitals in 27 countries were available for analysis. Death was more frequent amongst patients admitted
Background: Inflammatory breast cancer (IBC) is a rare and highly aggressive form of primary breast cancer. Little is known about the risk factors for IBC, specifically the association with socioeconomic position (SEP).. Methods: The association between breast cancer type (IBC vs. non-IBC) with county-level SEP in the Surveillance, Epidemiology, and End Results database for cases diagnosed from 2000 to 2007 was examined. County-level SEP characteristics included metropolitan versus non-metropolitan residence, percentage below the poverty level, percentage less than high-school graduate, and an index combining the poverty and high-school variables. IBC and non-IBC age-adjusted incidence rates were calculated, stratified on SEP and race/ethnicity. The odds of IBC versus non-IBC given a particular SEP characteristic, adjusting for age and race/ethnicity, was examined through fitting of hierarchical logistic regression models (HLM).. Results: Incidence rates for IBC generally increased as SEP ...
Although I enjoyed this paper quite a bit, and it seemed perfect fodder for some clever fun in photoshop (actually by E. Lu, see above), I also felt that that the study had some methodologically weak areas. For instance, the authors failed to take advantage of a new phylogenetic logistic regression procedure by Ives & Garland [2010], which seems ideally suited to their data. (In their defense, the method is brand new.) Consequently, however, the authors found themselves of the unfortunate position of using an arbitrary scoring system to estimate size-related reproductive skew: adding 1 point for the presence of pronounced sexual dichromatism, for example, and subtracting 1 point for alternative reproductive tactics (which might decrease the advantage of large male size) . With a phylogenetic multivariable logistic regression, the authors could have tested for an association between the log-odds of protogyny and each of their proxies for size-based reproductive skew (which also included ...
Background: Mental health has been a largely neglected issue among men who have sex with men (MSM) across the world. This study examines the prevalence and correlates of depression among MSM. Data and Methods: Data for this study are used from a cross-sectional Behavioral Tracking Survey-2012 conducted among 1176 MSM from Andhra Pradesh (undivided), a southern state of India. Depression of MSM was assessed using Patient Health Questionnaire-2 scale. Descriptive statistics, bivariate and multivariate logistic regression techniques were used for analysis. Results: More than one-third of MSMs (35%) in the survey reported to have depression. The likelihood of experiencing depression was 5 times higher among MSM who were mobile for sex work outside their place of residence (55% vs 17%, AOR: 5.2, 95% CI: 3.7 - 7.3) and had experienced physical or sexual violence (82% vs 33%, AOR: 6.0, 95% CI: 2.1 - 17.4) than their respective counterparts. Rates of depression were significantly higher among MSM who had
By Dr. Rafael Díaz and Dr. Teresa De la Cruz. Healthcare organizations around the world are under intense pressure to improve productivity while maintaining high safety standards. Logistics is a key part of their operations, so it stands to reason that improving the efficiency of logistics will help healthcare facilities to achieve their productivity goals. The Healthcare Logistics Education and Learning Pathway (HELP) project aims to make healthcare logistics more efficient through education.. One study estimates that 30% to 45% of hospital budgets (regardless of which management model they adhere to) is spent on logistics activities. Moreover, almost half of this cost could be reduced by adopting best logistics management practices without any reduction in service quality.. Yet many of the people who operate and manage healthcare facilities lack a formal education in the logistics discipline. Departmental managers might hold business degrees at masters or even PhD levels, but there is no ...
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We conducted a nationwide, multicenter, retrospective, observational study in patients ≥ 80 years with type 2 diabetes mellitus and COVID-19 hospitalized in 160 Spanish hospitals between March 1 and May 29, 2020 who were included in the SEMI-COVID-19 Registry. The primary outcome measure was in-hospital mortality. A multivariate logistic regression analysis were performed to assess the association between preadmission cardiometabolic therapy and in-hospital mortality. The regression analysis values were expressed as adjusted odds ratios (AOR) with a 95% confidence interval (CI). In order to select the variables, the forward selection Wald statistic was used. Discrimination of the fitted logistic model was assessed via a receiver operating characteristic (ROC) curve. The Hosmer-Lemeshow test for logistic regression was used to determine the models goodness of fit ...
Ever since S/4 HANA was announced there was mention about Simple Finance and Simple Logistics. After Simple Finance release by SAP there is fair amount of documentation around key data model simplification and thereby leading to performance optimisation, but hardly any details on Simple Logistics is available in public domain. At the same time from business standpoint Finance and Logistics go hand in hand and surely consultants / partners / customers are waiting to understand how Logistics will be Simplified. Drawing parallel from Simple Finance the expectation was to have key data model simplification and some key functional improvements in Logistics area. To compound the problem Logistics in core ERP is quite a broad functional area covering MM, PP, SD modules and lot of sub modules. In reality there is lot of expectation with the mention of Simple Logistics.. In continuation of learning and riding ahead in S/4 HANA journey, I have been exploring on Simple Logistics for past couple of ...
logistics News: Latest and Breaking News on logistics. Explore logistics profile at Times of India for photos, videos and latest news of logistics. Also find news, photos and videos on logistics
Transportation and Logistics News from Logistics World - Logistics World is a worldwide directory of freight transportation and logistics resources on the internet. Logistics World is the home of the WWW Virtual Library of Logistics, the WWW Virtual Library of Trucking, and the LogisticsWorld Logistics Business Directory.
This paper develops a localized approach to elastic net logistic regression, extending previous research describing a localized elastic net as an extension to a localized ridge regression or a localized lasso. All such models have the objective to capture data relationships that vary across space. Geographically weighted elastic net logistic regression is first evaluated through a simulation experiment and shown to provide a robust approach for local model selection and alleviating local collinearity, before application to two case studies: county-level voting patterns in the 2016 USA presidential election, examining the spatial structure of socio-economic factors associated with voting for Trump, and a species presence-absence data set linked to explanatory environmental and climatic factors at gridded locations covering mainland USA. The approach is compared with other logistic regressions. It improves prediction for the election case study only which exhibits much greater spatial ...
TY - JOUR. T1 - Technology credit scoring model with fuzzy logistic regression. AU - Sohn, So Young. AU - Kim, Dong Ha. AU - Yoon, Jin Hee. PY - 2016/6/1. Y1 - 2016/6/1. N2 - Technology credit scoring models have been used to screen loan applicant firms based on their technology. Typically a logistic regression model is employed to relate the probability of a loan default of the firms with several evaluation attributes associated with technology. However, these attributes are evaluated in linguistic expressions represented by fuzzy number. Besides, the possibility of loan default can be described in verbal terms as well. To handle these fuzzy input and output data, we proposed a fuzzy credit scoring model that can be applied to predict the default possibility of loan for a firm that is approved based on its technology. The method of fuzzy logistic regression as an appropriate prediction approach for credit scoring with fuzzy input and output was presented in this study. The performance of the ...
Abstract: Many researchers in the health field use the chi-square statistic to identify associations between variables. This edition of research notes will demonstrate that the odds ratio may be a preferred analysis to yield more useful and meaningful results. In epidemiological and health contexts, the outcome variable is often discrete, taking on two (or more) possible scores. Application of odds ratios and logistic models in epidemiology and medical research ...
When outcomes are binary, the c-statistic (equivalent to the area under the Receiver Operating Characteristic curve) is a standard measure of the predictive accuracy of a logistic regression model. An analytical expression was derived under the assumption that a continuous explanatory variable follows a normal distribution in those with and without the condition. We then conducted an extensive set of Monte Carlo simulations to examine whether the expressions derived under the assumption of binormality allowed for accurate prediction of the empirical c-statistic when the explanatory variable followed a normal distribution in the combined sample of those with and without the condition. We also examine the accuracy of the predicted c-statistic when the explanatory variable followed a gamma, log-normal or uniform distribution in combined sample of those with and without the condition. Under the assumption of binormality with equality of variances, the c-statistic follows a standard normal cumulative
Abstract This paper examines how school engagement influences the timing of family formation for youth. We pay particular attention to variation across four racial/ethnic groups and by generation status, variation that reflects the diversification of U.S. society through immigration. Using data from the National Education Longitudinal Study (NELS), we employ discrete-time multinomial logistic regression models examining the likelihood of childbearing or marriage in late adolescence. We find that the delaying effects of school enrollment and engagement vary by race/ethnicity, suggesting that strategies for socioeconomic success that focus on delaying family roles are more important among some groups than others. The results also indicate that controlling for school enrollment and school engagement reduces differences in early marriage and non-marital childbearing by generation status. ...
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First party logistics providers (1PL) are single service providers in a specific geographic area that specialize in certain goods or shipping methods. Examples are: carrying companies, port operators, depot companies. The logistics department of a producing firm can also be a first party logistics provider if they have own transport assets and warehouses.[10]. Second party logistics providers (2PL) are service providers which provide their specialized logistics services in a larger (national) geographical area than the 1PL do. Often there are frame contracts between the 2PL and the customer, which regulate the conditions for the transport duties that are mostly placed short term. 2PLs provide own and external logistics resources like trucks, forklifts, warehouses etc. for transport, handling of cargo or warehouse management activities.[10] Second party logistics arose in the course of the globalization and the uprising trend of lean management, when the companies began to outsource their ...
This lesson will focus more on performing a Logistic Regression in Python. If you are unfamiliar with Logistic Regression, check out my earlier lesson: Logistic Regression with Gretl If you would like to follow along, please download the exercise file here: logi2 Import the Data You should be good at this by now, use Pandas .read_excel(). df.head()…
Functions to assess the goodness of fit of binary, multinomial and ordinal logistic models. Included are the Hosmer-Lemeshow tests (binary, multinomial and ordinal) and the Lipsitz and Pulkstenis-Robinson tests (ordinal).
As we covered above, one of these predictors is binary and the other is continuous. This means we have to interpret the two a little bit differently. First is the binary score: gender. We first look at the p value. It is below .05, telling us that it is significant, and we can safely interpret the odds ratio. To interpret this result, we have to know what a 0 (low) and a 1 (high) correspond to, and our researcher recalls that she coded this as 0 = female, and 1 = male. She finds this to be a good thing because when the odds ratio is greater than 1, it describes a positive relationship. The positive relationship means that as gender increases, the odds of being eaten increases. Based on our coding, an increase in gender means a gender of 1 instead of 0 - in other words, being male. This can be interpreted to mean that being in the (1) group, or being male, puts you at 5 times greater odds of being eaten.. If the odds ratio for gender had been below 1, she would have been in trouble, as an ...
Multinomial Logistic Regression Predicted Probability Map To Visualize The Influence Of Socio-Economic Factors On Breast Cancer Occurrence in Southern Karnataka
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Interpret a correlation matrix. Know how to generate a regression equation. Understand average prediction error (residual difference).. Use a multiple regression model to predict a criterion* variable. Determine whether there is a relationship between the criterion* variable and the predictor** variables using in the regression model. Determine which predictor** variables make a significant contribution to the regression model. Interpret the coefficient of multiple determination. Interpret the partial regression coefficients (beta weights).. Understand how categorical predictor** variables can be included in the regression model. Understand regression models that include interaction terms. Recognize when multicollinearity is a problem and how it affects your regression model. Know when to use logistic regression to predict a criterion* variable. * Criterion variable is analogous with dependent variable, but is generally referred to as a criterion in correlational analyses. .** Predictor variable ...
Beta calibration: a well-founded and easily implemented improvement on logistic calibration for binary classifiersMeelis Kull, Telmo Silva Filho, P...
Median hsTnT levels were higher in patients aged ≥75 years of age (n=248) compared with younger (,75 years; n=434) patients (30.53 (13.72-67.51) versus 15.24 (4.90-41.74) pg·mL−1, respectively; p,0.001). In a multivariable logistic regression analysis, the predictive value of hsTnT ≥14 pg·mL−1 remained significant after adjustment for age, renal insufficiency and symptom duration (OR 14.26, 95% CI 1.87-108.53; p=0.010). By ROC analysis, an optimised hsTnT cut-off value of 12 pg·mL−1 for patients aged ,75 years (AUC 0.76, 95% CI 0.66-0.85; p=0.002), and of 45 pg·mL−1 for elderly patients (AUC 0.74, 95% CI 0.64-0.84; p=0.005) were calculated for prediction of an adverse 30-day outcome. Reclassification of elderly patients using the age-optimised cut-off value of 45 pg·mL−1 provided better risk prediction compared with the established hsTnT cut-off value of 14 pg·mL−1 (NRI 0.18, 95% CI 0.01-0.36; p=0.041) (table 2). Alternatively, in patients aged ,75 years, the age-optimised ...
Logistic Regression Book - Applied logistic regression / David W. Hosmer, Jr., Stanley Lemeshow. . When we worked on the First Edition of this book we were very lim-.
Background: We sought to identify personal and work-related predictors of upper extremity symptoms and related functional impairment among 1,108 workers employed for 6 months in a new job. Methods: We collected data at baseline and 6-month follow-up using self-administered questionnaires. Multivariate logistic regression models were created for each outcome variable. Predictors included personal r
Todays evidence is not new; is, in fact, well known. Well, make that just plain known. Its learned and then forgotten, dismissed. Everybody knows about these kinds of mistakes, but everybody is sure they never happen to them. Theyre too careful; theyre experts; they care.. Its too easy to generate significant answers which are anything but significant. Heres yet more-how much do you need!-proof. The pictures below show how easy it is to falsely generate significance by the simple trick of adding independent or control variables to logistic regression models, something which everybody does.. Lets begin!. Recall our series on selling fear and the difference between absolute and relative risk, and how easy it is to scream, But what about the children! using classical techniques. (Read that link for a definition of a p-value.) We anchored on EPAs thinking that an excess probability of catching some malady when exposed to something regulatable of around 1 in 10 thousand is ...
Results. There were 34 infants in each group. Both groups were similar in age, gender, cardiac defect type, ICU length of stay, and time interval to second stage or definitive repair. Shunt interventions (18 versus 32%, p=0.16), shunt thrombosis (14 versus 17%, p=0.74), and mortality (9 versus 12%, p=0.65) were not significantly different between groups. On multiple logistic regression analysis, single-ventricle morphology (odds ratio 5.2, 95% confidence interval of 1.2-23, p=0.03) and post-operative red blood cells transfusion ⩾24 hours [odds ratio 15, confidence interval of (3-71), p,0.01] were associated with shunt-related adverse events. High-dose acetylsalicylic acid treatment [odds ratio 2.6, confidence interval of (0.7-10), p=0.16] was not associated with decrease in these events. ...
Video created by Johns Hopkins University for the course Regression Models. This week, we will work on generalized linear models, including binary outcomes and Poisson regression. 2000+ courses from schools like Stanford and Yale - no ...
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Hilbe, J. M. (2009). Logistic Regression Models. Chapman & Hall/CRC Press. ISBN 978-1-4200-7575-5. Mika, S.; et al. (1999). " ... However, when discriminant analysis' assumptions are met, it is more powerful than logistic regression. Unlike logistic ... Edward Altman's 1968 model is still a leading model in practical applications. In computerised face recognition, each face is ... Logistic regression or other methods are now more commonly used. The use of discriminant analysis in marketing can be described ...
Landwehr, N.; Hall, M.; Frank, E. (2005). "Logistic Model Trees" (PDF). Machine Learning. 59 (1-2): 161-205. doi:10.1007/s10994 ... Muggeo, V. M. R. (2008). "Segmented: an R package to fit regression models with broken-line relationships" (PDF). R News. 8: 20 ... Muggeo, V. M. R. (2003). "Estimating regression models with unknown break‐points". Statistics in Medicine. 22 (19): 3055-3071. ...
Christensen, Ronald (1997). Log-linear models and logistic regression. Springer Texts in Statistics (Second ed.). New York: ... Discrete Statistical Models with Social Science Applications. North Holland, 1980. Bishop, Y. M. M.; Fienberg, S. E.; Holland, ...
ISBN 978-0-521-63201-0. Christensen, Ronald (1997). Log-linear models and logistic regression. Springer Texts in Statistics ( ... Other generalized linear models such as the negative binomial model or zero-inflated model may function better in these cases. ... A Poisson regression model is sometimes known as a log-linear model, especially when used to model contingency tables. Negative ... This model is popular because it models the Poisson heterogeneity with a gamma distribution. Poisson regression models are ...
Srivastava, P.W.; Shukla, R. (2008-09-01). "A Log-Logistic Step-Stress Model". IEEE Transactions on Reliability. 57 (3): 431- ... When the appropriate model is not known in advance, or there exist multiple accepted models, the test must estimate what model ... When the model is known in advance the test only needs to identify the parameters for the model, however it is necessary to ... its parameters) One would then use a known model or attempt to fit a model to relate how each stress factor influenced the ...
Standard statistical models, such as those involving the categorical distribution and multinomial logistic regression, assume ... Christensen, Ronald (1997). Log-linear models and logistic regression. Springer Texts in Statistics (Second ed.). New York: ... and separate regression models (logistic regression, probit regression, etc.). As a result, the term "categorical variable" is ... The identity of a particular word (e.g., in a language model): One of V possible choices, for a vocabulary of size V. For ease ...
Raju, N. S., Steinhaus, S. D., Edwards, J. E., & DeLessio, J. (1991). A logistic regression model for personnel selection. ... Raju, N. S., & Guttman, I. (1965). A new working formula for the split-half reliability model. Educational and Psychological ... Goldman, S. H., & Raju, N. S. (1986). Recovery of one- and two-parameter logistic item parameters: An empirical study. ... Clemans, W. V., Lunneborg, C. E., & Raju, N. S. (2004). Professor paul horst's legacy: A differential prediction model for ...
Methods for fitting such models include logistic and probit regression. Several statistics can be used to quantify the quality ... O'Connell, A. A. (2006). Logistic Regression Models for Ordinal Response Variables. SAGE Publications.. ... It is also used as a quality measure of binary choice or ordinal regression (e.g., logistic regressions) and credit scoring ... for binary classification or prediction of binary outcomes including binary choice models in econometrics. ...
Asadabadi, M. R., Saberi, M., & Chang, E. (2017, July). Logistic informatics modelling using concept of stratification (CST). ... Ghildyal, A., & Chang, E. IT Governance and Benefit Models: Literature Review and Proposal of a Novel Approach. Asadabadi, M. R ...
Yu, Chian-Son; Li, Han-Lin (2000). "A robust optimization model for stochastic logistic problems". International Journal of ... A very popular model of local robustness is the radius of stability model: ρ ^ ( x , u ^ ) := max ρ ≥ 0 { ρ : u ∈ S ( x ) , ∀ u ... Modern robust optimization deals primarily with non-probabilistic models of robustness that are worst case oriented and as such ... The non-probabilistic (deterministic) model has been and is being extensively used for robust optimization especially in the ...
One type would be the logistic regression model. In this model, positive coefficients increase the probability of truth while ... Graph-based models Lastly, these models focus on the relationships among reviewers, comments, products and so on. To evaluate ... Content-based models These models evaluate comment credibility by leveraging on language features taken from user comments and ... Behavior-based models These models often make use of the "indicative features of unreliable comments extracted from the ...
When the logistic regression model is used to model the case-control data and the odds ratio is of interest, both the ... ISBN 978-0-7817-5564-1. Prentice RL, Pyke R (1979). "Logistic disease incidence models and case-control studies". Biometrika. ...
ISBN 978-0-471-22618-5. Christensen, R. (1997). Log-Linear Models and Logistic Regression (2nd ed.). Springer. Petitjean, F.; ... The saturated model is the model that includes all the model components. This model will always explain the data the best, but ... Other possible models are the conditional equiprobability model and the mutual dependence model. Each log-linear model can be ... Log-linear analysis models can be hierarchical or nonhierarchical. Hierarchical models are the most common. These models ...
An Application of the Conditional Logistic Choice Model." Journal of Econometrics, vol. 121, no. 1-2: pp. 271-296. DOI: 10.1016 ...
Tree diameter distribution modelling: introducing the logit logistic distribution. Canadian Journal of Forest Research, 35(6), ... The log-logistic distribution, also known as the Fisk distribution in economics, is a special case of the log metalog where b l ... The logit-logistic distribution is a special case of the logit metalog where a i = 0 {\displaystyle a_{i}=0} for all i > 2 {\ ... Rewriting the logistic quantile function to incorporate the above substitutions for μ {\displaystyle \mu } and s {\displaystyle ...
He is best known for the logistic growth model. Verhulst developed the logistic function in a series of three papers between ... Population dynamics Logistic map Logistic distribution Verhulst, Pierre-François (1838). "Notice sur la loi que la population ... Although the continuous-time logistic equation is often compared to the logistic map because of similarity of form, it is ... Published as:Cramer, J. S. (2004). "The early origins of the logit model". Studies in History and Philosophy of Science Part C ...
... and 3 parameter logistic models as well as the partial credit model and generalized partial credit model. It can also generate ... 2 and 3 parameter logistic models, graded response models, partial credit and generalized partial credit models, rating scale ... IRTEQ supports various popular unidimensional IRT models: Logistic models for dichotomous responses (with 1, 2, or 3 parameters ... model framework; it can fit dichotomous or polytomous models, along with mixed models. It supports both exploratory and ...
His dissertation was titled Development and application of a multivariate logistic latent trait model. Reckase began his career ... Reckase, Mark (1972-01-01). "Development and Application of a Multivariate Logistic Latent Trait Model". Psychology - ... Reckase, M. (1979). Unifactor latent trait models applied to multifactor tests: Results and implications. Journal of ... his dissertation was on the early development of a multidimensional item response model and he went on to write a book on the ...
The OR plays an important role in the logistic model. Imagine there is a rare disease, afflicting, say, only one in many ... Logistic regression is one way to generalize the odds ratio beyond two binary variables. Suppose we have a binary response ... Due to the widespread use of logistic regression, the odds ratio is widely used in many fields of medical and social science ... If we use multiple logistic regression to regress Y on X, Z1, ..., Zp, then the estimated coefficient β ^ x {\displaystyle {\ ...
Kubinger, K.D. (2009). Application of the Linear Logistic Test Model in Psychometric Research. Educational and Psychological ... Kubinger, K.D. (2009). Applications of the Linear Logistic Test Model in Psychometric Research. Educational and Psychological ... Hohensinn, C. & Kubinger, K.D. (2011). On the impact of missing values on item fit and the model validness of the Rasch model. ... Kubinger, K.D., Rasch, D. & Yanagida, T. (2009). On designing data-sampling for Rasch model calibrating an achievement test. ...
The simplest model for chaotic dynamics is the logistic map. Self-adjusting logistic map dynamics exhibit adaptation to the ... 2000). "Adaptation to the edge of chaos in the self-adjusting logistic map". Phys. Rev. Lett. 84 (26): 5991-5993. arXiv:nlin/ ... Ranjit Kumar Upadhyay (2009). "Dynamics of an ecological model living on the edge of chaos". Applied Mathematics and ... 1994). "A theory for adaptation and competition applied to logistic map dynamics". Physica D. 75 (1-3): 343-360. Bibcode: ...
The discriminative model is a logistic regression maximum entropy classifier. With the discriminative model, the goal is to ... A generative model, as the name suggests, allows one to generate new data points ( x , y ) {\displaystyle (x,y)} . The joint ... The discriminative approach for modeling substitution probabilities, P ( a , C l ) {\displaystyle P(a,C_{l})} where C l {\ ... "Discriminative Modelling of Context-specific Amino Acid Substitution Properties" BIOINFORMATICS 28.24 (2012): 3240-247. Oxford ...
"Fitting logistic models under case-control or choice-based sampling". Journal of the Royal Statistical Society, Series B. 48 (2 ... The model is then built on this biased sample. The effects of the input variables on the target are often estimated with more ... Model Assisted Survey Sampling.CS1 maint: multiple names: authors list (link) Scheaffer, Richard L., William Mendenhal and R. ... Model Assisted Survey Sampling.CS1 maint: multiple names: authors list (link) Shahrokh Esfahani, Mohammad; Dougherty, Edward ( ...
Other models suggest exponential growth, logistic growth, or other functions. Another example of hyperbolic growth can be found ... It has been also demonstrated that the hyperbolic models of this type may be used to describe in a rather accurate way the ... These functions can be confused, as exponential growth, hyperbolic growth, and the first half of logistic growth are convex ... "International Journal of Mathematical Models and Methods in Applied Sciences". 2016. Vol. 10, pp. 200-209 . The 21st Century ...
Pierre François Verhulst formulated the logistic growth model in 1836. Fritz Müller described the evolutionary benefits of what ... 1986). "Computer Models and Automata Theory in Biology and Medicine" (PDF). Mathematical Modeling : Mathematical Models in ... of apoptosis Modelling physiological systems Modelling of arterial disease Multi-scale modelling of the heart Modelling ... computer modeling in biology and medicine, arterial system models, neuron models, biochemical and oscillation networks, quantum ...
Scores are computed based on the three-parameter logistic model. The exam is divided into two sessions, each lasting four hours ... ICFES manages the logistics of the exam but contracts different companies transport valuables that ensure the integrity of the ...
Scott, A.J.; Wild, C.J. (1986). "Fitting logistic models under case-control or choice-based sampling". Journal of the Royal ... Model Assisted Survey Sampling.. CS1 maint: Multiple names: authors list (link). *^ Scheaffer, Richard L., William Mendenhal ... The model is then built on this biased sample. The effects of the input variables on the target are often estimated with more ... Särndal, Carl-Erik, and Swensson, Bengt, and Wretman, Jan (1992). Model assisted survey sampling. Springer-Verlag. ISBN 0-387- ...
It supports common models such as logistic regression and decision trees. Version 0.8 was published in 2015. Subsequent ... It focuses on model development, so it includes model producers and PFA manipulation tools in addition to runtime execution. ... Model development in Jython) - Antinous is a model-producer plugin for Hadrian that allows Jython code to be executed anywhere ... two complementary standards that simplify the deployment of analytic models. "Portable Format for Analytics: moving models to ...
"Evaluating sediment chemistry and toxicity data using logistic regression modeling." Environ Toxicol Chem 18:1311-1322. ... and a logistical model. ESBs do not predict bioaccumulation or trophic transfer to wildlife and humans, which are important ...
Her dissertation, supervised by David F. Andrews, was Generalized Logistic Models. She became an assistant professor of ...
Logistic function. *Malthusian growth model. *Maximum sustainable yield. *Overpopulation in wild animals ... Ecologists use simplified one trophic position food chain models (producer, carnivore, decomposer). Using these models, ... Ecological Modelling. 220 (11): 1376-1382. doi:10.1016/j.ecolmodel.2009.03.005. Archived from the original (PDF) on 2011-10-07. ... Using these models they can measure and test for generalized patterns in the structure of real food web networks. Ecologists ...
The two "Deck Majors" are Marine Transportation and Maritime Logistics and Security. Marine transportation students learn about ... Navy's David Taylor Model Basin; inducted into USMMA Hall of Distinguished Graduates in 2008 ... intermodal logistics, marine engineering, maritime law, maritime insurance, or defense contracting.[citation needed] ...
Liao, Xingmiu; Tsai, Wen-Hsuan (2019). "Clientelistic State Corporatism: The United Front Model of "Pairing-Up" in the Xi ... Logistic Support Dept.. *Equipment Development Dept.. *Training & Administration Dept.. *National Defense Mobilization Dept. ...
Generalized linear model. *Exponential families. *Logistic (Bernoulli) / Binomial / Poisson regressions. Partition of variance ... Rasch, G. (1960). Probabilistic models for some intelligence and attainment tests. Copenhagen: Danish Institute for Educational ... Further progress was made by Georg Rasch (1960), who developed the probabilistic Rasch model that provides a theoretical basis ... For example, applications of measurement models in educational contexts often indicate that total scores have a fairly linear ...
"Saano Dumre Revisited: Changing Models of Illness in a Village of Central Nepal." Contributions to Nepalese Studies 28(2): 155- ... The government has provided all vaccines and immunization related logistics without any cost to hospitals, the private ...
Generalized linear model. *Exponential families. *Logistic (Bernoulli) / Binomial / Poisson regressions. Partition of variance ...
Van Geert assumed that the basic growth model is the so-called "logistic growth model", which suggests that the development of ... named the model of hierarchical complexity (MHC). The model assesses a single measure of difficulty of inferred tasks across ... a b c Demetriou, A., Mouyi, A., & Spanoudis, G. (2008). Modeling the structure and development of g. Intelligence, 5, 437-454. ... The functional shift models explains how new units are created leading to stage change in the fashion described by Case[9] and ...
Logistic function. *Malthusian growth model. *Maximum sustainable yield. *Overpopulation in wild animals ...
The 31st flew the single seat Convair F-102 Delta Dagger, which, like the later model F-89s of the 445th, was equipped with ... Air Force Global Logistics Support Center (AFGLSC) (former). *Air Force Security Assistance Center (AFSAC) ... Other projects of the 412th included XF-35A Lightning II and Boeing X-32, competing models for the Joint Strike Fighter program ... software and components as well as modeling and simulation for the USAF. It is also the host wing for Edwards Air Force Base, ...
... but also a face-off model connecting personnel in engineering, procurement, operations, quality and logistics with their ... While there is no one correct model for deploying SRM at an organizational level, there are sets of structural elements that ... The SRM office and supply chain function are typically responsible for defining the SRM governance model, which includes a ... managing logistics and delivery, collaborating on product design, etc. The starting point for defining SRM is a recognition ...
"Fifth Party Logistic Model (5PL)". LogisticsGlossary. Retrieved 21 September 2018.. *^ Expedited Transportation: Just in Time ( ... Lead logistics providersEdit. 3PL providers without their own assets are called lead logistics providers. Lead logistics ... Third-party logistics (abbreviated 3PL, or sometimes TPL) in logistics and supply chain management is a company's use of third- ... The logistics department of a producing firm can also be a first party logistics provider if they have own transport assets and ...
a b c d e f g h i j Zigmont, J. J., Kappus, L. J., & Sudikoff, S. N. (2011, April). The 3D model of debriefing: defusing, ... Attend to logistic details. *Declare & enact a commitment to respecting learners & concern for their psychological safety[32] ... While many models for debriefing exist, they all follow, at a minimum, a three-phase format.[1][13][22] Debriefing models can ... the 3D Model,[23] the GAS model,[29] and Diamond Debrief.[27] ... While all debriefing models include the phases of the three- ...
Command & logistics support ship (two more to be commisioned) Etna class AOR - Replenishment oiler Etna A 5326 13,400 tonnes ... New operating model for CMRE research vessels, *^ ... Command & logistics support ship Stromboli class AOR - Replenishment oiler (small) Stromboli A 5327 9,100 tonnes Vesuvio to be ... Logistic tender ships[edit]. Class Picture Type Ship Pennant. no. Displacement Notes ...
... role modeling, classical conditioning, operant conditioning, and brutalization.[4] ... Logistics Command Pacific and the Integrated Support Command Center - Alameda. ...
Generalized linear model. *Exponential families. *Logistic (Bernoulli) / Binomial / Poisson regressions. Partition of variance ...
Logistic function. *Malthusian growth model. *Maximum sustainable yield. *Overpopulation. *Overexploitation. *Population cycle ... Otero-Muras, I.; Franco-Uría, A.; Alonso, A.A.; Balsa-Canto, E. (2010). "Dynamic multi-compartmental modelling of metal ... can be predicted by models.[3][4] Hypotheses for molecular size cutoff criteria for use as bioaccumulation potential indicators ... "Predicting Concentrations of Organic Chemicals in Fish by Using Toxicokinetic Models". Environmental Science & Technology. 46 ...
Analyzing the circuit using Kirchhoff's circuit laws, the dynamics of Chua's circuit can be accurately modeled by means of a ... In this case a chaotic attractor in mathematical model can be obtained numerically, with relative ease, by standard ... The easy experimental implementation of the circuit, combined with the existence of a simple and accurate theoretical model, ...
Newton, Steven H. (2006), Hitler's Commander: Field Marshal Walter Model - Hitler's Favorite General, Cambridge, MA: Da Capo, ... Oral history interview with Frank Ladwig, an officer who worked on the logistics of the Battle of the Bulge from the Veterans ... Model and von Rundstedt both believed aiming for Antwerp was too difficult, given Germany's lack of resources in late 1944. At ... They developed plans that did not aim to cross the Meuse River; Model's being Unternehmen Herbstnebel (Operation Autumn Mist) ...
In this model, the change in population density of the two mutualists is quantified as: d. N. 1. d. t. =. r. 1. N. 1. −. α. 11 ... This is equivalent to inverse of the carrying capacity, 1/K, of N, in the logistic equation. ... This model is most effectively applied to free-living species that encounter a number of individuals of the mutualist part in ... Mathematical modelingEdit. Mathematical treatments of mutualisms, like the study of mutualisms in general, has lagged behind ...
The logistic map is used either directly to model population growth, or as a starting point for more detailed models of ... Logistic map[edit]. An example of a recurrence relation is the logistic map: x. n. +. 1. =. r. x. n. (. 1. −. x. n. ). ,. {\ ... See also logistic map, dyadic transformation, and tent map. Relationship to differential equations[edit]. When solving an ... Simulation, Modelling and Optimization, SMO'06. pp. 399-404.. *. Polyanin, Andrei D. "Difference and Functional Equations: ...
The logistics of universal healthcare vary by country. Some programs are paid for entirely out of tax revenues. In others tax ... Under this model, citizens have free range to choose hospitals and physicians without using a gatekeeper and do not have to ... The new mixed economy Russia has switched to a mixed model of health care with private financing and provision running ... During the 1980s, Medibank Public was renamed Medicare by the Hawke Labor government, which also changed the funding model, to ...
The key problem in the formalization and modeling of knowledge economy is a vague definition of knowledge, which is a rather ... With Earth's depleting natural resources, the need for green infrastructure, a logistics industry forced into just-in-time ... Their use depends on individual and group preferences (see the cognitive IPK model) which are "economy-dependent".[13] ... developing algorithms and simulated models, and innovating on processes and systems. Harvard Business School Professor, Michael ...
Generalized linear model. *Exponential families. *Logistic (Bernoulli) / Binomial / Poisson regressions. Partition of variance ... The cache language models and other statistical language models used in natural language processing to assign probabilities to ... Such quantities can be modeled using a mixture distribution. Related to real-valued quantities that grow linearly (e.g. errors ... Well-known discrete probability distributions used in statistical modeling include the Poisson distribution, the Bernoulli ...
Luxury and logistics at Antioch, 162?-65[edit]. Antioch from the southwest (engraving by William Miller after a drawing by H. ... Statue of Lucius Verus on a body modelled after a sculpture by the ancient Athenian sculptor Myron, Vatican Museums ...
According to the standard model, in which a monopolist sets a single price for all consumers, the monopolist will sell a lesser ... The Standard Oil trust streamlined production and logistics, lowered costs, and undercut competitors. "Trust-busting" critics ... In 2000, the De Beers business model changed due to factors such as the decision by producers in Russia, Canada and Australia ... Most economic textbooks follow the practice of carefully explaining the perfect competition model, mainly because this helps to ...
These typically need to be close to where the children live, for practical logistics. ... Stand level modelling. *Stratification (vegetation). *Subalpine forest. *Taiga, a biome characterized by coniferous forests ...
The United States Geological Survey (USGS) has released a California earthquake forecast,[25] which models earthquake ... Other industries include software, automotive, ports, finance, biomedical, and regional logistics. The region was a leader in ...
Modula - System Logistics: An engineering and manufacturing company which designs and builds automated storage equipment used ... This began the transformation from a small farming town into a textile manufacturing center on the model of Lowell, ...
It adopted a conscription model drawing on elements from the Israeli and Swiss national conscription schemes. Some 9,000 male ... Logistics, Land divisions, Airport Police Division (APD). Those posted to the Police Coast Guard (PCG) or Police KINS will ... information and communications and logistics specialists or instructors (such as Physical Training Instructors) among many ... NSFs having at least NITEC certificates who perform exceptionally well are recommended to undergo the Situational Test model to ...
Generalized linear model. *Exponential families. *Logistic (Bernoulli) / Binomial / Poisson regressions. Partition of variance ... This can be done for instance using test-retest,[19] quasi-simplex,[20] or mutlitrait-multimethod models.[21] ... A user-centric model of voting intention from social media. Proceedings of the 51st Annual Meeting of the Association for ... Another source of error stems from faulty demographic models by pollsters who weigh their samples by particular variables such ...
We use a stagewise fitting process to construct the logistic regression models that can select relevant attributes in the data ... and show how this approach can be used to build the logistic regression models at the leaves by incrementally refining those ... For predicting numeric quantities, there has been work on combining these two schemes into model trees, i.e. trees that ... In this paper, we present an algorithm that adapts this idea for classification problems, using logistic regression instead of ...
... as well as the order in which they are introduced into the model. ... to be used in stepwise selection logistic regression modeling ... how to do a logistic regression model in both PROC GENMOD and PROC LOGISTIC; and how to present and interpret your linear and ... Its called logistic regression models_V3_complete. … And its in your exercise files for this movie. … As you might have ... See this first model? … This is the simple model with just the diabetes variable … in it. … See here, I put the estimate for ...
We demonstrate that there are infinitely many equivalent ways to specify a model. An implication is that there may well be many ... This paper is about the Linear Logistic Test Model (LLTM). ... Fischer, G.H. (1995). The linear logistic test model. In G.H. ... This paper is about the Linear Logistic Test Model (LLTM). We demonstrate that there are infinitely many equivalent ways to ... Bechger, T.M., Verhelst, N.D., & Verstralen, H.F.M. (2001). Identifiability of nonlinear logistic test models.Psychometrika, 66 ...
3D logistic models for download, files in 3ds, max, c4d, maya, blend, obj, fbx with low poly, animated, rigged, game, and VR ... Free 3D Models 3ds Max Models Maya Models Cinema 4D Models Blender Models ... Top 3D Model Categories. Airplane Anatomy Animal Architecture Car Characters Food & Drink Furnishings Industrial Interior ...
... Masaki Sekiguchi,1 Emiko Ishiwata,2 and Yukihiko Nakata3 ... Masaki Sekiguchi, Emiko Ishiwata, and Yukihiko Nakata, "Convergence of a Logistic Type Ultradiscrete Model," Discrete Dynamics ...
Models:. Library. Community. Modeling Commons. User Manuals:. Web. Printable Chinese. Czech. Japanese. NetLogo Models Library: ... Sample Models/System Dynamics. (back to the library) Logistic Growth. If you download the NetLogo application, this model is ... For the model itself:. *Wilensky, U. (2005). NetLogo Logistic Growth model. ... RELATED MODELS. System Dynamics -, Exponential Growth. HOW TO CITE. If you mention this model or the NetLogo software in a ...
... M. Elena Nenni. Department of Industrial Engineering, University of ... 4. The Cost Model. Lets go through the core of the approach analyzing the cost model. We present firstly the data input and ... Specific goals of the model are the provision of the annual cost of ILS activities through a specific cost model and a ... Department of Defense (DOD), Directive 4100.35, Development of Integrated Logistics Support for Systems and Equipments, ...
The basic features of a logistic growth rate are deeply influenced by the carrying capacity of the system and the changes are ... Two extensions of stochastic logistic model for fish growth have been examined. ... Two extensions of stochastic logistic model for fish growth have been examined. The basic features of a logistic growth rate ... M. Shah, "Stochastic Logistic Model for Fish Growth," Open Journal of Statistics, Vol. 4 No. 1, 2014, pp. 11-18. doi: 10.4236/ ...
Module 2 covers how to estimate linear and logistic model parameters using survey data. After completing this module, you will ... Models. Module 2 covers how to estimate linear and logistic model parameters using survey data. After completing this module, ... Logistic Models in R. To view this video please enable JavaScript, and consider upgrading to a web browser that supports HTML5 ... In this video we will illustrate how to fit a logistic model in R. So Im going to use the same data set that weve seen before ...
Related Threads on Differential Equations - Logistic Model Logistic growth model, differential equation ...
Logistic model may refer to: Logistic function - a continuous sigmoidal curve Logistic map - a discrete version, which exhibits ... chaotic behavior Logistic regression This disambiguation page lists articles associated with the title Logistic model. If an ...
This yields a logistic regression in which the outcome is case or control, and the predictor variables include the number of ... In a case-control study, the model can be constructed so that each coefficient gives the log odds ratio for disease for an ...
Logistic model trees are based on the earlier idea of a model tree: a decision tree that has linear regression models at its ... a logistic model tree (LMT) is a classification model with an associated supervised training algorithm that combines logistic ... Niels Landwehr, Mark Hall, and Eibe Frank (2003). Logistic model trees (PDF). ECML PKDD.CS1 maint: uses authors parameter (link ... doi:10.1007/s10994-005-0466-3. Sumner, Marc, Eibe Frank, and Mark Hall (2005). Speeding up logistic model tree induction (PDF ...
Nuria Diaz-Tena & Frank Potter & Michael Sinclair & Stephen Williams, "undated". "Logistic Propensity Models to Adjust for ... Nonresponse weighting ; Propensity Modeling ; Weighting Classes ; Community Trackiing Study ; Physician Surveys; JEL ...
Fundamentals of Quantitative Modeling. This module explores regression models, which allow you to start with data and discover ... You might find a logistic regression model much much more appropriate. If we were to fit a logistic model for this data, which ... So, heres the fit of the logistic regression model, and once you have got that fit. You can see how you can use it for ... If were going to create a realistic model for such outcomes. And heres the methodology. Its logistic regression. Its ...
We extend the model selection principle introduced by Birgé and Massart (2001) to logistic regression model. This selection is ... This paper is devoted to model selection in logistic regression. ... MODEL SELECTION IN LOGISTIC REGRESSION Marius Kwemou 1, 2 Marie ... We extend the model selection principle introduced by Birgé and Massart (2001) to logistic regression model. This selection is ... Keywords : projection AMS 2000 MSC: Primary 62J02 logistic regression model selection 62F12 Secondary 62G05 62G20 ...
... the worlds leading provider of digital 3D models for visualization, films, television, and games. ... Logistics Drone 3D Model available on Turbo Squid, ... Free 3D Models 3ds Max Models Maya Models Cinema 4D Models ... Top 3D Model Categories. Airplane Anatomy Animal Architecture Car Characters Food & Drink Furnishings Industrial Interior ... Its a detailed photoreal logistics drone.. You can use 3ds Max for rendering with and V-Ray and Maxwell Render.. You can also ...
... parameter logistic model (3PL) in IRT but am unsure how to ,,, best fit it from a logistic regression in R. , , -- , Brian D. ... Pinheiro and Bates discuss a three-parameter logistic growth model ,, in their ,, Mixed Effects Models in S and S-PLUS, but as ... Original Message----- ,,, To: r-help at ,,, Subject: [R] logistic regression and 3PL model ,,, ,,, Hello ... R] logistic regression and 3PL model. Dimitris Rizopoulos dimitris.rizopoulos at Thu Nov 25 17:51:30 CET ...
As ... - Selection from Applied Logistic Regression, 3rd Edition [Book] ... The Multiple Logistic Regression Model 2.1 Introduction In Chapter 1 we introduced the logistic regression model in the context ... Chapter 2: The Multiple Logistic Regression Model. 2.1 Introduction. In Chapter 1 we introduced the logistic regression model ... the multivariable or multiple logistic regression model). Central to the consideration of the multiple logistic models is ...
... assessing models, treating missing values, and using efficiency techniques for massive data sets. ... This course covers predictive modeling using SAS/STAT software with emphasis on the LOGISTIC procedure. This course also ... Predictive Modeling Using Logistic Regression (V9.3 and V14.2) 16.0 Stunden. 180 Tage Englisch. 1,180 CHF. ... Use logistic regression to model an individuals behavior as a function of known inputs. *Create effect plots and odds ratio ...
... assessing models, treating missing values and using efficiency techniques for massive data sets. ... This course covers predictive modeling using SAS/STAT software with emphasis on the LOGISTIC procedure. This course also ... use logistic regression to model an individuals behavior as a function of known inputs *create effect plots and odds ratio ... Predictive Modeling Using Logistic Regression 16.0 óra. 180 nap English. 219,775 HUF. ...
It should read Logistic Modeling -- help integrating/solving for P... ... Related Threads on Logistic modeling - help integrating/solving for P Integrating for logistic growth model ... Sorry about the title; I accidentally hit enter instead of Shift. It should read Logistic Modeling -- help integrating/ ...
Math·AP®︎/College Calculus BC·Differential equations·Logistic models with differential equations ... And so let me just draw a little graph here to show the typical solution to a logistic differential equation. So this is our ... Now why is this a model that youll see a lot, especially why is it useful for studying things like populations. When a ... Well you could go back to the logistic differential equation. You can see that its really our rate of change is a function, ...
One way to think about how to model this is just so what is the rate of change of population with respect to time? How does ... Voiceover] Lets think a little bit about modeling population and what I have pictures here are some of the most known, ... He was a Belgian mathematician who read Malthus’ work and tried to model the behavior that Malthus was talking about that, ... In his mind the more natural or more realistic function to model population would look something like this or even potentially ...
Robust estimators for logistic regression are alternative techniques due to their robustness. This paper presents a new class ... Logistic regression is the most important tool for data analysis in various fields. The classical approach for estimating ... of robust techniques for logistic regression. They are weighted maximum likelihood estimators which are considered as Mallows- ... model 1), 5% of the data are contaminated (model 2), 10% moderate contaminated (model 3) and 20% extreme contaminated model ( ...
Discover how this SAP Model Company service offers a ready-to-run, comprehensive reference solution. Take advantage of the ... SAP Model Company for Logistics Execution. Discover how this SAP Model Company service offers a ready-to-run, comprehensive ...
Reservoir models, which previously yielded reasonable results for reserves est ... Logistic Growth Models. Logistic growth curves are a family of mathematical models used to forecast growth in numerous ... The logistic growth model does not extrapolate to non-physical values.. Introduction. One source of production to meet the ... This paper presents a new method for empirically forecasting production based on the logistic growth model.. ...
Previous message: [R] Finite Mixture Models with logistic regression *Next message: [R] Finite Mixture Models with logistic ... Previous message: [R] Finite Mixture Models with logistic regression *Next message: [R] Finite Mixture Models with logistic ... R] Finite Mixture Models with logistic regression. Spencer Graves spencer.graves at Sat Jul 9 17:34:52 CEST 2005 * ... Do we have any R package that can do analysis on finite mixture model with , logistic regression? Thanks , , Faith , , Feng Gao ...
The Logistic Regression Model In this chapter we will consider regression models when the regressand is dichotomous or binary ... In this chapter we will consider regression models when the regressand is dichotomous or binary in nature. The data is of the ... In the previous chapter we considered the linear regression model where the regressand was assumed to be continuous along with ...
  • Hosmer DW, Lemeshow S (2000) Applied logistic regression. (
  • Samples of brake pedal speed, Brake pedal displacement, and vehicle acceleration measured from panic and normal situations, will be fed for training neural networks and acquiring logistic regression equation. (
  • In this course you will learn how to use survey weights to estimate descriptive statistics, like means and totals, and more complicated quantities like model parameters for linear and logistic regressions. (
  • Optimal response modeling is studied using logistic regression, random forests, and I* algorithm of building tuned regressions. (
  • The model may be represented by a series of logistic regressions for dependent binary variables, with common regression parameters reflecting the proportional odds assumption. (
  • In the framework of conditional density estimation, we use candidates taking the form of mixtures of Gaussian regressions with logistic weights and means depending on the covariate. (
  • Hi, I m trying to build a credit risk model (an application scorecard) based on the logistic regression for my master thesis. (
  • Odds ratios based on the logistic-regression results were calculated for these variables. (
  • The book covers, very completely, the nuances of regression modeling with particular emphasis on binary and ordinal logistic regression and parametric and nonparametric survival analysis. (
  • The proportional odds model for ordinal logistic regression provides a useful extension of the binary logistic model to situations where the response variable takes on values in a set of ordered categories. (
  • K. Matsuya and M. Kanai, Exact solution of a delay difference equation modeling traffic flow and their ultra-discrete limit , . (
  • R. Willox, "Modelling natural phenomena with discrete and ultradiscrete systems," in Proceedings of the RIAM Symposium Held at Chikushi Campus, Kyushu Universiy 22AO-S8 , pp. 13-22. (
  • Logistic model may refer to: Logistic function - a continuous sigmoidal curve Logistic map - a discrete version, which exhibits chaotic behavior Logistic regression This disambiguation page lists articles associated with the title Logistic model. (
  • An additional modeling consideration, which is introduced in this chapter, is using design variables for modeling discrete, nominal scale, independent variables. (
  • Traditional predictions of radioactive waste transport using discrete fracture network (DFN) models often consider one particular realization of the fracture distribution based on fracture statistic features. (
  • With a successful model in hand, and continuing focus on the total landed cost, you can put on your tactical hat, and fuss over discrete tactical functions. (
  • Have experience building statistical models using SAS software. (
  • To provide an understanding of the statistical principles behind, and the practical application of, univariable and multivariable linear and logistic regression in medical, epidemiological and health services research. (
  • Revision of basic methods in a statistical modelling framework. (
  • Professor Harrell has produced a book that offers many new and imaginative insights into multiple regression, logistic regression and survival analysis, topics that form the core of much of the statistical analysis carried out in a variety of disciplines, particularly in medicine. (
  • Statistical models based on the scale-invariance (or scaling) concept has increasingly become an essential tool for modeling extreme rainfall processes over a wide range of time scales. (
  • Results of this assessment based on different statistical criteria have indicated the comparable performance of the proposed scaling GLO model as compared to other popular models in practice. (
  • Different statistical models have been created to include litter effect, with many undergoing constant improvement (Yamamoto and Yanagimoto, 1994). (
  • An often overlooked problem in building statistical models is that of endogeneity, a term arising from econometric analysis, in which the value of one independent variable is dependent on the value of other predictor variables. (
  • Multivariate logistic regression is a statistical method commonly used in several fields to build predictive models. (
  • These algorithms are associated with traditional statistical models. (
  • To create a statistical model to predict LM versus AM in children based on history, physical, and laboratory findings during the initial presentation of meningitis. (
  • A total of 175 children with meningitis were included in the final statistical model. (
  • Regression models are the key tools in predictive analytics, and are also used when you have to incorporate uncertainty explicitly in the underlying data. (
  • This course covers predictive modeling using SAS/STAT software with emphasis on the LOGISTIC procedure. (
  • This data shows tuning logistic regression using random forest variable importance results in an optimal predictive model even with data without interaction effects. (
  • Logistic regression has been applied in many machine learning applications to build building predictive models. (
  • The Hosmer-Lemeshow test revealed a good fit for the model, and the Nagelkerke R 2 effect size demonstrated good predictive efficacy. (
  • EDITOR,-The application of multiple regression models in medical research has greatly increased during the past years. (
  • You'll examine correlation and linear association, methodology to fit the best line to the data, interpretation of regression coefficients, multiple regression, and logistic regression. (
  • In this chapter, we generalize the model to one with more than one independent variable (i.e., the multivariable or multiple logistic regression model). (
  • Instead of presenting isolated techniques, this text emphasizes problem solving strategies that address the many issues arising when developing multivariable models using real data and not standard textbook examples. (
  • If you mention this model or the NetLogo software in a publication, we ask that you include the citations below. (
  • Presumably you are simulating the data so that you can call PROC LOGISTIC and obtain parameter estimates and other statistics for the simulated data. (
  • So, optionally, Step 5 is to write the data to a SAS data set so that PROC LOGISTIC can read it. (
  • In this example, 150 observations were generated so that you can run PROC LOGISTIC against the simulated data and see that the parameter estimates are close to the parameter values. (
  • The maximum likelihood estimator is a common technique of parameter estimation in the binary regression model. (
  • There are two main parameters, $N$, the total number of virions produced by one infected cell, and $r$, the logistic parameter which controls the growth rate. (
  • model evaluates the four-parameter logistic function and its gradient. (
  • For small samples, there is a lot of uncertainty in the parameter estimates for a logistic regression. (
  • Under the BBL model, expected responses follow a logistic function which can be made equal to that of the Four Parameter Logistic (4PL) model. (
  • And then we will see how to test whether subset of coefficients is zero, the same way we did in the linear model. (
  • Central to the consideration of the multiple logistic models is estimating the coefficients and testing for their significance. (
  • Interpretation of model coefficients as differences in means or odds ratios. (
  • The analysis of the various coefficients was done across all models. (
  • We extend the model selection principle introduced by Birgé and Massart (2001) to logistic regression model. (
  • Everitt B., Rabe-Hesketh S. (2001) Generalized Linear Models I: Logistic Regression. (
  • 7] proposed a natural class of robust estimator and testing procedures for binomial models and Poisson models, which are based on a concept of quasi-likelihood estimator proposed by [8]. (
  • 1989). Haseman and Soares (1976) concluded that, when analyzing experiments that look at dichotomous fetal responses, binomial or Poisson models provide poor fits, as there is similarity between responses from the same litter (Kupper et al. (
  • The beta-binomial model, considered by Williams (1975), is commonly used to account for littermate correlation when analyzing dose response data (Kupper et al. (
  • Multinomial and binomial logistic regression models are used, and different versions of the models are compared and assessed with cross validation. (
  • This Lagrange multiplier test is similar to the modification index used in structural equation modeling. (
  • The detection and correction of specification errors in structural equation models. (
  • Narrator] The population P of T of bacteria in a petry dish satisfies the logistic differential equation. (
  • Let's just remind ourselves what we're talking about or what they're talking about with the logistic differential equation and the carrying capacity. (
  • So in general a logistic differential equation is one where we seeing the rate of change of, and it's often referring to population, so let's just stick with population. (
  • And so let me just draw a little graph here to show the typical solution to a logistic differential equation. (
  • one way is we can actually put our logistic differential equation in this form and then we can recognize what the carrying capacity is. (
  • As T approaches infinity, this thing approaches zero and so we can think from this logistic differential equation what P values would make this thing be zero based on this differential equation or when this thing approaches zero, what P values would this approach. (
  • It is in a form that is related to the number $3/2$ and the coupling strength, and thus, is comparable to the well-known $3/2$ condition for the uncontrolled delayed logistic equation. (
  • Unbounded and blow-up solutions for a delay logistic equation with positive feedback. (
  • Based on an equation that models animal population behavior it creates compelling or chaotic IDM sequences in Ableton Live. (
  • Logistic is based on an equation that predicts animal population behavior, also called the Logistic Equation. (
  • Despite its simplicity, the logistic equation can generate complex behavior that becomes chaotic (you can find more details here ). (
  • 9] studied the breakdown of the maximum likelihood estimator in the logistic model. (
  • In this article we investigate the use of weight functions introduced by [17] as a weight function for Mallows type (weighted maximum likelihood estimator) to obtain a robust estimation for logistic regression, in addition, to compare their performance with classical maximum likelihood estimator and some existing robust methods by means of simulation study and real data sets. (
  • The maximum likelihood estimator for the logistic regression model is given in Section 2. (
  • The asymptotic normality of maximum likelihood estimators (MLEs) is obtained even though the support of this non-regular regression model depends on unknown parameters. (
  • Logistic regression is a proper analysis method to model the data and explain the relationship between the binary response variable and explanatory variables. (
  • In this chapter we will consider regression models when the regressand is dichotomous or binary in nature. (
  • It is widely believed that regression models for binary responses are problematic if we want to compare estimated coeffcients from models for different groups or with different explanatory variables. (
  • The first arises if the binary model is treated as an estimate of a model for an unobserved continuous response, and the second when models are compared between groups which have different distributions of other causes of the binary response. (
  • An approach is described arising from comparisons of the separate (correlated) fits to the binary logistic models underlying the overall model. (
  • Linear mixed models (LMMs) are widely used in genome-wide association studies (GWASs) to account for population structure and relatedness, for both continuous and binary traits. (
  • To overcome this problem, we develop a computationally efficient logistic mixed model approach for genome-wide analysis of binary traits, the generalized linear mixed model association test (GMMAT). (
  • This approach fits a logistic mixed model once per GWAS and performs score tests under the null hypothesis of no association between a binary trait and individual genetic variants. (
  • The script works after arbitrary logit or logistic commands. (
  • Transform the linear predictor by the logistic (inverse logit) function. (
  • Two extensions of stochastic logistic model for fish growth have been examined. (
  • M. A. Shah and U. Sharma, "Optimal Harvesting Policies for a Generalized Gordon-Schaefer Model in Randomly Varying Environment," Applied Stochastic Models in Business and Industry, John Wiley & Sons, Ltd., 2002. (
  • In this context, we develop an innovative analytical model in a stochastic environment to assist managers with talent planning in their organizations. (
  • We first develop a multi-period mixed integer nonlinear programming model and then exploit chance-constrained programming to a linearized instance of the model to handle stochastic parameters, which follow any arbitrary distribution functions. (
  • This video reviews the variables to be used in stepwise selection logistic regression modeling in this demonstration. (
  • The dataset is relatively small, and the authors use stepwise logistic regression models to detect small differences. (
  • Module 2 covers how to estimate linear and logistic model parameters using survey data. (
  • We also cover the features of survey data sets that need to be accounted for when estimating standard errors of estimated model parameters. (
  • Logistic models have been used to assess predictability of pregnancy loss using ultrasound parameters as dependable variables. (
  • One model including 566 gravidas, 7.9% of whom had an early pregnancy, identified HR and CRL as the most significant parameters to predict a pregnancy loss, together with maternal age and vaginal bleeding 8 . (
  • Its parameters can be related to covariates such as dose and gender through a regression model. (
  • The Ballooned Beta-logistic (BBL) model expands the response boundaries from (0,1) to (L,U), where L and U are unknown parameters. (
  • In addition to this, an approach based upon a hierarchical process model is shown that supports acquisition of input data necessary for configuring a logistics process and setting its parameters. (
  • The above data set is entered in RGA (using the Reliability data type) and the model parameters are calculated, as shown next. (
  • We use a stagewise fitting process to construct the logistic regression models that can select relevant attributes in the data in a natural way, and show how this approach can be used to build the logistic regression models at the leaves by incrementally refining those constructed at higher levels in the tree. (
  • To illustrate this phenomenon, we analyze a real data set using a Lagrange multiplier test for the specification of the model. (
  • In this video we will illustrate how to fit a logistic model in R. So I'm going to use the same data set that we've seen before, this academic performance data set that comes bundled with the R survey package. (
  • Logistic regression model for analyzing extended haplotype data. (
  • 1 Nevertheless, assessing the accuracy of regression models in describing the data (goodness of fit) is almost unknown in medical research. (
  • This module explores regression models, which allow you to start with data and discover an underlying process. (
  • This course also discusses selecting variables and interactions, recoding categorical variables based on the smooth weight of evidence, assessing models, treating missing values, and using efficiency techniques for massive data sets. (
  • Logistic regression is the most important tool for data analysis in various fields. (
  • The new model is easy to use, and it is very capable of trending existing production data and providing reasonable forecasts of future production. (
  • His research interests lie in the areas of epidemic logistics, data analysis and medical operations management. (
  • It includes imputation methods for dealing with missing data effectively, methods for dealing with nonlinear relationships and for making the estimation of transformations a formal part of the modeling process, methods for dealing with "too many variables to analyze and not enough observations," and powerful model validation techniques based on the bootstrap. (
  • Working through the case studies in the book will demonstrate what can be achieved with a little imagination, when modelling complex and challenging data sets. (
  • In this paper we highlight a data augmentation approach to inference in the Bayesian logistic regression model. (
  • To determine the factors influencing landslides, data layers of geology, surface materials, land cover, and topography were analyzed by logistic regression analysis, and the results are used for landslide hazard mapping. (
  • Using the UML modelling tools such as component and class diagrams, the model addresses the highest level quality business processes and data structure as well as all identified providing and depending interfaces and the messaging system in a module-based framework design. (
  • The methodology and adopted procedures are explained in details of which provide a better understanding of the modelling and the possibility for the lower-levels quality data extensions following the same framework. (
  • The model is able to manipulate all quality control data for purchasing, production and remedy operation in a lot-based make-to-order production system within a defined module. (
  • The feasibility and accuracy of this model were assessed using ER data from a network of 21 raingages located in Ontario, Canada. (
  • As Bieler and Williams (1975) state, littermate correlation can have a huge impact on how toxicology data is modeled and analyzed. (
  • From generating consumer insights to understanding the product flows comprising driver behaviour, shortest routes, and other valuable information, big data has resulted in tremendous cost saves and optimized delivery models. (
  • This paper describes the socioeconomic determinants of primary school dropout in Uganda with the aid of a logistic model analysis using the 2004 National Service Delivery Survey data. (
  • Logistics looks like a very complex area, but for a Data Modeller, it seems to have just a few Entities. (
  • In my book Simulating Data with SAS , I show how to use the SAS DATA step to simulate data from a logistic regression model. (
  • Recently there have been discussions on the SAS/IML Support Community about simulating logistic data by using the SAS/IML language. (
  • This article describes how to efficiently simulate logistic data in SAS/IML, and is based on the DATA step example in my book. (
  • The SAS/IML statements for simulating logistic data are concise. (
  • Sales Datasheet for the Teradata Transportation and Logistics Data Model (TLDM), which is a licensed customer offering which provides the map showing the pieces of information required to support Use Cases that challenge your business. (
  • The TLDM models the enterprise business data, data relationships, business rules governing these data relationships, and Transportation and Logistics industry-specific topic areas. (
  • At Georgia Tech's Integrated Food Center, we have been studying various ways of representing and modeling the food chain that incorporate both process and data standardization to provide a means to answer the above questions. (
  • Last month's Reliability Basics article introduced success/failure data in the context of developmental testing reliability growth analysis and listed the models that can be used to analyze such data. (
  • In this month's article, we continue the discussion by highlighting the logistic model, available in RGA , and show its application to model success/failure data as well as another type of developmental growth data that consists of reliability values at different times or stages. (
  • Reliability data can be analyzed with various models, such as Lloyd Lipow (which was covered in the Reliability Basics article in Issue 52), Gompertz, modified Gompertz and the logistic model (which is the subject of this article). (
  • This article explained a process for analyzing failure/success and reliability data from developmental reliability growth tests using the logistic growth model. (
  • Hence, medical journals may be publishing papers in which regression models are misused or results are misinterpreted. (
  • We investigated the use of logistic regression in papers published in the BMJ, JAMA, the Lancet, and the New England Journal of Medicine during 1991-4. (
  • Improved Testing And Specification Of Smooth Transition Regression Models ," Working Papers 210, University of California, Davis, Department of Economics. (
  • A Floor and Ceiling Model of U.S. Output ," Cambridge Working Papers in Economics 9407, Faculty of Economics, University of Cambridge. (
  • The previous papers were considering the problem as a single source logistic network problem while in real world the people face a multi source logistic network problem. (
  • A new LM specification procedure to choose between Logistic and Exponential Smooth Transition Autoregressive (STAR) models is introduced. (
  • Please find Alko Online Store logistic model and Listing procedure and retail sale of alcoholic beverages 1st May 2016 document in attachments below. (
  • The SAS documentation for the LOGISTIC procedure includes a brief discussion of the mathematics of logistic regression . (
  • In this paper, we present an algorithm that adapts this idea for classification problems, using logistic regression instead of linear regression. (
  • We compare the performance of our algorithm against that of decision trees and logistic regression on 32 benchmark UCI datasets, and show that it achieves a higher classification accuracy on average than the other two methods. (
  • In computer science, a logistic model tree (LMT) is a classification model with an associated supervised training algorithm that combines logistic regression (LR) and decision tree learning. (
  • Have completed a statistics course that covers linear regression and logistic regression, such as the Statistics 1: Introduction to ANOVA, Regression, and Logistic Regression course. (
  • In this chapter, we introduce generalized linear models , which include the regression and ANOVA models of previous chapters, but can also be used for modeling non-normally distributed response variables, in particular categorical variables. (
  • Testing nonlinearity: Decision rules for selecting between logistic and exponential STAR models ," Spanish Economic Review , Springer;Spanish Economic Association, vol. 3(3), pages 193-209. (
  • Identifiability of nonlinear logistic test models. (
  • Transforming categorical variables into either WOE (weight of evidence) or probability of response coupled with equal frequency binning of size 10 results in improved models. (
  • For a dynamical model for the evolution of the carrying capacity of the vasculature formulated in [15] optimal controls are computed for both a Gompertzian and logistic model of tumor growth. (
  • While optimal controls for the Gompertzian model typically contain a segment along which the control is singular, for the logistic model optimal controls are bang-bang with at most two switchings. (
  • Robustness of optimal controls for a class of mathematical models for tumor anti-angiogenesis. (
  • A fractional-order delay differential model with optimal control for cancer treatment based on synergy between anti-angiogenic and immune cell therapies. (
  • Optimal and suboptimal protocols for a mathematical model for tumor anti-angiogenesis in combination with chemotherapy. (
  • Optimal control for a mathematical model for anti-angiogenic treatment with Michaelis-Menten pharmacodynamics. (
  • Penalisation of long treatment time and optimal control of a tumour growth model of Cahn-Hilliard type with singular potential. (
  • Optimal control of normalized SIMR models with vaccination and treatment. (
  • This is a surprising and important result for automated optimal logistic regression model building. (
  • The Low-Level Waste Model evaluates the optimal transportation policy for shipping waste directly from the source to a final destination without any intermediate stops. (
  • The Spent Fuel Logistics Model evaluates the optimal transportation policy for shipping unreprocessed spent fuel from nuclear power plants (1) indirectly, that is, to an Away-From-Reactor (AFR) storage facility, with subsequent transhipment to a repository, or (2) directly to a repository. (
  • Most popular research includes the optimal routing technique [3] and the logistic tracking by using wireless telecommunication technology based on RFID or GPS [4] for more reliable service. (
  • A logistic regression model is developed within the framework of a Geographic Information System (GIS) to map landslide hazards in a mountainous environment. (
  • The results from this study demonstrate that the use of a logistic regression model within a GIS framework is useful and suitable for landslide hazard mapping in large mountainous geographic areas such as the southern Mackenzie Valley. (
  • This paper deepens the understanding regarding the practical usability of CM models for purchasing decisions, and provides a framework for determining a desired complexity of cost management in different purchasing environments. (
  • In their study, the authors consider the high computation capabilities of GPU and easy development onto Open Computing Language (OpenCL) framework to execute logistic training process. (
  • City logistics long-term planning: simulation of shopping mobility and goods restocking and related support systems Agostino Nuzzolo, Antonio Comi and Luca Rosati 8. (
  • From small businesses to multinational companies, organizations apply simulation models to analyze logistics networks, reduce costs and improve customer service. (
  • At this seminar in Munich, we will showcase our flagship products for managing logistics challenges - AnyLogic simulation platform and anyLogistix supply chain simulation and analytics software - along with their adoption case studies. (
  • Our European partners will also demo, with hands-on examples, how they leverage simulation to model and optimize complex logistics systems. (
  • Solaymani Roody, S., "Modeling Drivers' Behavior During Panic Braking for Brake Assist Application, Using Neural Networks and Logistic Regression and a Comparison," SAE Technical Paper 2011-01-2384, 2011, . (
  • Non-parametric tests and logistic regression models were used for comparisons of distributions and testing of associations. (
  • Tree induction methods and linear models are popular techniques for supervised learning tasks, both for the prediction of nominal classes and continuous numeric values. (
  • Ahmed, I. and Cheng, W. (2020) The Performance of Robust Methods in Logistic Regression Model. (
  • Many different types of models and methods are discussed. (
  • In this study, a model of drivers' behavior during a severe braking is created using both neural networks and logistic regression methods to determine the BAS threshold activation. (
  • Table 4 Mortality predictors of unplanned extubation using logistic regression model. (
  • The results of this study clearly show that the presence of bloody diarrhea is an endogenous variable in the model showing predictors of hemolytic uremic syndrome, in that the diarrhea is shown to be predicted by, and therefore strongly correlated with, several other variables used to predict hemolytic uremic syndrome. (
  • If the Gaussian or other model is more consistent, the dots should deviate from the diagonal. (
  • Gaussian model seems ruled out, and Logistic ELO model for computer chess engines seems to stand well on this try. (
  • These models make use of normal, student- t and generalized logistic distribution, see Rathie and Swamee [Technical Research Report No. 07/2006. (
  • Olinto, G. Applications of Skew Models Using Generalized Logistic Distribution. (
  • Rathie PN, Silva P, Olinto G. Applications of Skew Models Using Generalized Logistic Distribution. (
  • Furthermore, the Generalized Logistic distribution (GLO) has been recommended in UK for modeling of extreme hydrologic variables. (
  • This is a model of a logistic growth curve using the System Dynamics Modeler. (
  • Convergence of global and bounded solutions of a two-species chemotaxis model with a logistic source. (
  • We demonstrate that there are infinitely many equivalent ways to specify a model. (
  • Next, we use an empirical study to validate the model with a large global manufacturing company, and demonstrate how the proposed model can effectively manage talents in a practical context. (
  • The model was used to estimate the reliability throughout the test and estimate additional trials needed to demonstrate a certain reliability goal. (
  • This paper will propose a new empirical model for production forecasting in extremely low permeability oil and gas reservoirs based on logistic growth models. (
  • For predicting numeric quantities, there has been work on combining these two schemes into 'model trees', i.e. trees that contain linear regression functions at the leaves. (
  • Logistic model trees are based on the earlier idea of a model tree: a decision tree that has linear regression models at its leaves to provide a piecewise linear regression model (where ordinary decision trees with constants at their leaves would produce a piecewise constant model). (
  • The performance of two classifiers, logistic regression and neural networks, are compared for modeling noncatastrophic individual tree mortality for 21 species of trees in West Virginia. (
  • and how to present and interpret your linear and logistic regression models. (
  • This paper is about the Linear Logistic Test Model (LLTM). (
  • The linear logistic test model. (
  • This is the same function we used for the linear model. (
  • As in the case of linear regression, the strength of the logistic regression model is its ability to handle many variables, some of which may be on different measurement scales. (
  • 12] introduced a fast algorithm based on breakdown points of the trimmed likelihood for the generalized linear model. (
  • 15] generalized optimally bounded score functions studied by [16] for linear models to the logistic model. (
  • In the previous chapter we considered the linear regression model where the regressand was assumed to be continuous along with the assumption of normality for the error distribution. (
  • Modelling Non-Linear Economic Relationships ," OUP Catalogue , Oxford University Press, number 9780198773207. (
  • Common features of linear and logistic regression models. (
  • The linear model does not contain an error term, as would be the case for linear regression. (
  • The engines were distanced between themselves by an order of 200 ELO points each, so that each individual ELO interval between them is almost linear in ELO-score and independent of the ELO model. (
  • See here, I put the estimate for diabetes and next to it … I put the confidence interval and since we just ran … this model in our 600 code we know that diabflag … is statistically significant, here are the numbers. (
  • You'll also see how logistic regression will allow you to estimate probabilities of success. (
  • The new model incorporates known physical volumetric quantities of oil and gas into the forecast to constrain the reserve estimate to a reasonable quantity. (
  • The model can be used to estimate when the reliability goal of 99% will be achieved if testing and improvements continue. (
  • Therefore, the main objective of the present study is to propose a scaling GLO model for modeling ER processes over different time scales. (
  • Global dynamic urbanization trends have transformed the existing logistics supply chain, increasing its complexity, however technological penetration has enabled simplification of industry processes. (
  • Work the supply chain backward from the customer, one function at a time-keep in mind that every supply chain process or function you compromise from the model affects other processes, either positively or negatively. (
  • The I* algorithm is enhanced using a 0way interaction option to tune logistic regression without interaction effects. (
  • This paper, tried to find the minimum cost of fMLN using Imperialist Competitive Algorithm (ICA) with considering a multi source flexible multistage logistics network. (
  • Ruiz Estrada, Mario Arturo and Koutronas, Evangelos, The Multi-Dimensional Ports Stock Inventory and Logistic Control Graphical Modeling (March 13, 2019). (
  • A sufficient condition is established for globally asymptotic stability of the positive equilibrium of a regulated logistic growth model with a delay in the state feedback. (
  • The result is FIRM, Food Integration Reference Model, a three-level hierarchical model for modeling, designing, communicating, and evaluating the food chain. (
  • After model estimation, marginal effects for each of the models were obtained. (
  • On the optimality of singular controls for a class of mathematical models for tumor anti-angiogenesis. (
  • Mathematical modeling of the logistics of waste shipment is an effective way to provide input to program planning and long-range waste management. (
  • This problem is shown by model-generated coefficient standard errors that are larger than true standard errors, which biases the interpretation towards the null hypothesis and increases the likelihood of a type II error. (
  • Conduct post-hoc interpretation on models. (
  • Post-hoc interpretation can be applied on both simple and black box models. (
  • Using the intended meaning will facilitate the interpretation of models. (
  • The cost savings benefit to the Owner and the Developer can be exponential through the information embedded in the model over the life cycle of the project from the build phase to the Facilities Management phase. (
  • know the basis on which analytical strategy and model choice is made, and how the results should be interpreted. (
  • inproceedings{HIC2018:Scale_Invariance_Generalized_Logistic_GLO, author = {Truong-Huy Nguyen and Van-Thanh-Van Nguyen}, title = {Scale-Invariance Generalized Logistic (GLO) Model for Estimating Extreme Design Rainfalls in the Context of Climate Change}, booktitle = {HIC 2018. (
  • This book is the first work to conduct the emergency logistics optimization problem under the epidemic environment (whether natural or man-made), which provides a new perspective for the application of optimization theory. (
  • The authors take epidemic outbreak as the research object and deeply integrate the epidemic spread model with the optimization model of emergency resource scheduling, which opens up a novel application area of operations research. (
  • Application of exact route optimization for the evaluation of a city logistics truck ban scheme Ali Gul Qureshi, Eiichi Taniguchi, Russell G. Thompson and Joel S.E. Teo 3. (
  • Overall, it was determined that when the dependent variable has a rare occurrence, optimization of the mixed model fitting cannot be completed and the tests for dose effects cannot be conducted. (
  • The three volumes of this work cover the theory and foundations necessary to understand the key challenges of optimization of logistics flows. (
  • Several logistics models have been developed for use in parametric studies, contingency planning, and management of transportation networks. (