AnatomyDiseasesChemicals and DrugsAnalytical, Diagnostic and Therapeutic Techniques and EquipmentPsychiatry and PsychologyPhenomena and ProcessesDisciplines and OccupationsAnthropology, Education, Sociology and Social PhenomenaTechnology, Industry, AgricultureInformation ScienceHealth Care
Data Interpretation, StatisticalPlant BarkReproducibility of ResultsSoftwareOligonucleotide Array Sequence AnalysisAlgorithmsComputer SimulationGene Expression ProfilingComputational BiologySequence Analysis, DNAPolymerase Chain ReactionSensitivity and SpecificityObserver VariationImage Interpretation, Computer-AssistedRadiologyTime FactorsDiagnostic ErrorsModels, BiologicalEquipment DesignMaps as TopicConfidence IntervalsDevelopmental DisabilitiesProbabilityModels, StatisticalToxapheneArthritis, PsoriaticArthritis, RheumatoidArthritisMethotrexateArthritis, JuvenileArthritis, ExperimentalWetlandsLakesPsychology, AppliedOrthodontic Appliance DesignFresh WaterDecision MakingResearchForensic GeneticsGenetics, PopulationCriminologyForensic MedicineForensic AnthropologyY ChromosomeNational Institutes of Health (U.S.)Climatic ProcessesHydrologyCyclonic StormsUncertaintyModels, TheoreticalTidal Volume
Data Interpretation, Statistical: Application of statistical procedures to analyze specific observed or assumed facts from a particular study.Plant Bark: The outer layer of the woody parts of plants.Reproducibility of Results: 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.Software: Sequential operating programs and data which instruct the functioning of a digital computer.Oligonucleotide Array Sequence Analysis: Hybridization of a nucleic acid sample to a very large set of OLIGONUCLEOTIDE PROBES, which have been attached individually in columns and rows to a solid support, to determine a BASE SEQUENCE, or to detect variations in a gene sequence, GENE EXPRESSION, or for GENE MAPPING.Algorithms: A procedure consisting of a sequence of algebraic formulas and/or logical steps to calculate or determine a given task.Computer Simulation: Computer-based representation of physical systems and phenomena such as chemical processes.Gene Expression Profiling: The determination of the pattern of genes expressed at the level of GENETIC TRANSCRIPTION, under specific circumstances or in a specific cell.Computational Biology: A field of biology concerned with the development of techniques for the collection and manipulation of biological data, and the use of such data to make biological discoveries or predictions. This field encompasses all computational methods and theories for solving biological problems including manipulation of models and datasets.Sequence Analysis, DNA: A multistage process that includes cloning, physical mapping, subcloning, determination of the DNA SEQUENCE, and information analysis.Polymerase Chain Reaction: In vitro method for producing large amounts of specific DNA or RNA fragments of defined length and sequence from small amounts of short oligonucleotide flanking sequences (primers). The essential steps include thermal denaturation of the double-stranded target molecules, annealing of the primers to their complementary sequences, and extension of the annealed primers by enzymatic synthesis with DNA polymerase. The reaction is efficient, specific, and extremely sensitive. Uses for the reaction include disease diagnosis, detection of difficult-to-isolate pathogens, mutation analysis, genetic testing, DNA sequencing, and analyzing evolutionary relationships.Sensitivity and Specificity: 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)Observer Variation: The failure by the observer to measure or identify a phenomenon accurately, which results in an error. Sources for this may be due to the observer's missing an abnormality, or to faulty technique resulting in incorrect test measurement, or to misinterpretation of the data. Two varieties are inter-observer variation (the amount observers vary from one another when reporting on the same material) and intra-observer variation (the amount one observer varies between observations when reporting more than once on the same material).Image Interpretation, Computer-Assisted: Methods developed to aid in the interpretation of ultrasound, radiographic images, etc., for diagnosis of disease.Radiology: A specialty concerned with the use of x-ray and other forms of radiant energy in the diagnosis and treatment of disease.Time Factors: Elements of limited time intervals, contributing to particular results or situations.Diagnostic Errors: Incorrect diagnoses after clinical examination or technical diagnostic procedures.Models, Biological: 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.Equipment Design: Methods of creating machines and devices.Maps as Topic: Representations, normally to scale and on a flat medium, of a selection of material or abstract features on the surface of the earth, the heavens, or celestial bodies.Confidence Intervals: 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.Developmental Disabilities: Disorders in which there is a delay in development based on that expected for a given age level or stage of development. These impairments or disabilities originate before age 18, may be expected to continue indefinitely, and constitute a substantial impairment. Biological and nonbiological factors are involved in these disorders. (From American Psychiatric Glossary, 6th ed)Probability: The study of chance processes or the relative frequency characterizing a chance process.Models, Statistical: 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.Toxaphene: A very complex, but reproducible mixture of at least 177 C10 polychloro derivatives, having an approximate overall empirical formula of C10-H10-Cl8. It is used as an insecticide and may reasonably be anticipated to be a carcinogen: Fourth Annual Report on Carcinogens (NTP 85-002, 1985). (From Merck Index, 11th ed)Arthritis, Psoriatic: A type of inflammatory arthritis associated with PSORIASIS, often involving the axial joints and the peripheral terminal interphalangeal joints. It is characterized by the presence of HLA-B27-associated SPONDYLARTHROPATHY, and the absence of rheumatoid factor.Arthritis, Rheumatoid: A chronic systemic disease, primarily of the joints, marked by inflammatory changes in the synovial membranes and articular structures, widespread fibrinoid degeneration of the collagen fibers in mesenchymal tissues, and by atrophy and rarefaction of bony structures. Etiology is unknown, but autoimmune mechanisms have been implicated.ArthritisMethotrexate: An antineoplastic antimetabolite with immunosuppressant properties. It is an inhibitor of TETRAHYDROFOLATE DEHYDROGENASE and prevents the formation of tetrahydrofolate, necessary for synthesis of thymidylate, an essential component of DNA.Arthritis, Juvenile: Arthritis of children, with onset before 16 years of age. The terms juvenile rheumatoid arthritis (JRA) and juvenile idiopathic arthritis (JIA) refer to classification systems for chronic arthritis in children. Only one subtype of juvenile arthritis (polyarticular-onset, rheumatoid factor-positive) clinically resembles adult rheumatoid arthritis and is considered its childhood equivalent.Arthritis, Experimental: ARTHRITIS that is induced in experimental animals. Immunological methods and infectious agents can be used to develop experimental arthritis models. These methods include injections of stimulators of the immune response, such as an adjuvant (ADJUVANTS, IMMUNOLOGIC) or COLLAGEN.Wetlands: Environments or habitats at the interface between truly terrestrial ecosystems and truly aquatic systems making them different from each yet highly dependent on both. Adaptations to low soil oxygen characterize many wetland species.Lakes: Inland bodies of still or slowly moving FRESH WATER or salt water, larger than a pond, and supplied by RIVERS and streams.Psychology, Applied: The science which utilizes psychologic principles to derive more effective means in dealing with practical problems.Orthodontic Appliance Design: The planning, calculation, and creation of an apparatus for the purpose of correcting the placement or straightening of teeth.Fresh Water: Water containing no significant amounts of salts, such as water from RIVERS and LAKES.Decision Making: The process of making a selective intellectual judgment when presented with several complex alternatives consisting of several variables, and usually defining a course of action or an idea.Research: Critical and exhaustive investigation or experimentation, having for its aim the discovery of new facts and their correct interpretation, the revision of accepted conclusions, theories, or laws in the light of newly discovered facts, or the practical application of such new or revised conclusions, theories, or laws. (Webster, 3d ed)Forensic Genetics: The application of genetic analyses and MOLECULAR DIAGNOSTIC TECHNIQUES to legal matters and crime analysis.Genetics, Population: The discipline studying genetic composition of populations and effects of factors such as GENETIC SELECTION, population size, MUTATION, migration, and GENETIC DRIFT on the frequencies of various GENOTYPES and PHENOTYPES using a variety of GENETIC TECHNIQUES.Criminology: The study of crime and criminals with special reference to the personality factors and social conditions leading toward, or away from crime.Forensic Medicine: The application of medical knowledge to questions of law.Forensic Anthropology: Scientific study of human skeletal remains with the express purpose of identification. This includes establishing individual identity, trauma analysis, facial reconstruction, photographic superimposition, determination of time interval since death, and crime-scene recovery. Forensic anthropologists do not certify cause of death but provide data to assist in determination of probable cause. This is a branch of the field of physical anthropology and qualified individuals are certified by the American Board of Forensic Anthropology. (From Am J Forensic Med Pathol 1992 Jun;13(2):146)Y Chromosome: The male sex chromosome, being the differential sex chromosome carried by half the male gametes and none of the female gametes in humans and in some other male-heterogametic species in which the homologue of the X chromosome has been retained.National Institutes of Health (U.S.): An operating division of the US Department of Health and Human Services. It is concerned with the overall planning, promoting, and administering of programs pertaining to health and medical research. Until 1995, it was an agency of the United States PUBLIC HEALTH SERVICE.Climatic Processes: Characteristic events occurring in the ATMOSPHERE during the interactions and transformation of various atmospheric components and conditions.Hydrology: Science dealing with the properties, distribution, and circulation of water on and below the earth's surface, and atmosphere.Cyclonic Storms: Non-frontal low-pressure systems over tropical or sub-tropical waters with organized convection and definite pattern of surface wind circulation.Uncertainty: The condition in which reasonable knowledge regarding risks, benefits, or the future is not available.Models, Theoretical: 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.Tidal Volume: The volume of air inspired or expired during each normal, quiet respiratory cycle. Common abbreviations are TV or V with subscript T.

A method for calculating age-weighted death proportions for comparison purposes. (1/9497)

OBJECTIVE: To introduce a method for calculating age-weighted death proportions (wDP) for comparison purposes. MATERIALS AND METHODS: A methodological study using secondary data from the municipality of Sao Paulo, Brazil (1980-1994) was carried out. First, deaths are weighted in terms of years of potential life lost before the age of 100 years. Then, in order to eliminate distortion of comparisons among proportions of years of potential life lost before the age of 100 years (pYPLL-100), the denominator is set to that of a standard age distribution of deaths for all causes. Conventional death proportions (DP), pYPLL-100, and wDP were calculated. RESULTS: Populations in which deaths from a particular cause occur at older ages exhibit lower wDP than those in which deaths occur at younger ages. The sum of all cause-specific wDP equals one only when the test population has exactly the same age distribution of deaths for all causes as that of the standard population. CONCLUSION: Age-weighted death proportions improve the information given by conventional DP, and are strongly recommended for comparison purposes.  (+info)

A review of statistical methods for estimating the risk of vertical human immunodeficiency virus transmission. (2/9497)

BACKGROUND: Estimation of the risk of vertical transmission of human immunodeficiency virus (HIV) has been complicated by the lack of a reliable diagnostic test for paediatric HIV infection. METHODS: A literature search was conducted to identify all statistical methods that have been used to estimate HIV vertical transmission risk. Although the focus of this article is the analysis of birth cohort studies, ad hoc studies are also reviewed. CONCLUSIONS: The standard method for estimating HIV vertical transmission risk is biased and inefficient. Various alternative analytical approaches have been proposed but all involve simplifying assumptions and some are difficult to implement. However, early diagnosis/exclusion of infection is now possible because of improvements in polymerase chain reaction technology and complex estimation methods should no longer be required. The best way to analyse studies conducted in breastfeeding populations is still unclear and deserves attention in view of the many intervention studies being planned or conducted in developing countries.  (+info)

Statistical inference by confidence intervals: issues of interpretation and utilization. (3/9497)

This article examines the role of the confidence interval (CI) in statistical inference and its advantages over conventional hypothesis testing, particularly when data are applied in the context of clinical practice. A CI provides a range of population values with which a sample statistic is consistent at a given level of confidence (usually 95%). Conventional hypothesis testing serves to either reject or retain a null hypothesis. A CI, while also functioning as a hypothesis test, provides additional information on the variability of an observed sample statistic (ie, its precision) and on its probable relationship to the value of this statistic in the population from which the sample was drawn (ie, its accuracy). Thus, the CI focuses attention on the magnitude and the probability of a treatment or other effect. It thereby assists in determining the clinical usefulness and importance of, as well as the statistical significance of, findings. The CI is appropriate for both parametric and nonparametric analyses and for both individual studies and aggregated data in meta-analyses. It is recommended that, when inferential statistical analysis is performed, CIs should accompany point estimates and conventional hypothesis tests wherever possible.  (+info)

Incidence and duration of hospitalizations among persons with AIDS: an event history approach. (4/9497)

OBJECTIVE: To analyze hospitalization patterns of persons with AIDS (PWAs) in a multi-state/multi-episode continuous time duration framework. DATA SOURCES: PWAs on Medicaid identified through a match between the state's AIDS Registry and Medicaid eligibility files; hospital admission and discharge dates identified through Medicaid claims. STUDY DESIGN: Using a Weibull event history framework, we model the hazard of transition between hospitalized and community spells, incorporating the competing risk of death in each of these states. Simulations are used to translate these parameters into readily interpretable estimates of length of stay, the probability that a hospitalization will end in death, and the probability that a nonhospitalized person will be hospitalized within 90 days. PRINCIPAL FINDINGS: In multivariate analyses, participation in a Medicaid waiver program offering case management and home care was associated with hospital stays 1.3 days shorter than for nonparticipants. African American race and Hispanic ethnicity were associated with hospital stays 1.2 days and 1.0 day longer than for non-Hispanic whites; African Americans also experienced more frequent hospital admissions. Residents of the high-HIV-prevalence area of the state had more frequent admissions and stays two days longer than those residing elsewhere in the state. Older PWAs experienced less frequent hospital admissions but longer stays, with hospitalizations of 55-year-olds lasting 8.25 days longer than those of 25-year-olds. CONCLUSIONS: Much socioeconomic and geographic variability exists both in the incidence and in the duration of hospitalization among persons with AIDS in New Jersey. Event history analysis provides a useful statistical framework for analysis of these variations, deals appropriately with data in which duration of observation varies from individual to individual, and permits the competing risk of death to be incorporated into the model. Transition models of this type have broad applicability in modeling the risk and duration of hospitalization in chronic illnesses.  (+info)

Quantitative study of the variability of hepatic iron concentrations. (5/9497)

BACKGROUND: The hepatic iron concentration (HIC) is widely used in clinical practice and in research; however, data on the variability of HIC among biopsy sites are limited. One aim of the present study was to determine the variability of HIC within both healthy and cirrhotic livers. METHODS: Using colorimetric methods, we determined HIC in multiple large (microtome) and small (biopsy-sized) paraffin-embedded samples in 11 resected livers with end-stage cirrhosis. HIC was also measured in multiple fresh samples taken within 5 mm of each other ("local" samples) and taken at sites 3-5 cm apart ("remote" samples) from six livers with end-stage cirrhosis and two healthy autopsy livers. RESULTS: The within-organ SD of HIC was 13-1553 microg/g (CV, 3.6-55%) for microtome samples and 60-2851 microg/g (CV, 15-73%) for biopsy-sized samples. High variability of HIC was associated with mild to moderate iron overload, because the HIC SD increased with increasing mean HIC (P <0.002). Livers with mean HIC >1000 microg/g exhibited significant biological variability in HIC between sites separated by 3-5 cm (remote sites; P <0.05). The SD was larger for biopsy-sized samples than for microtome samples (P = 0.02). CONCLUSION: Ideally, multiple hepatic sites would be sampled to obtain a representative mean HIC.  (+info)

A simulation study of confounding in generalized linear models for air pollution epidemiology. (6/9497)

Confounding between the model covariates and causal variables (which may or may not be included as model covariates) is a well-known problem in regression models used in air pollution epidemiology. This problem is usually acknowledged but hardly ever investigated, especially in the context of generalized linear models. Using synthetic data sets, the present study shows how model overfit, underfit, and misfit in the presence of correlated causal variables in a Poisson regression model affect the estimated coefficients of the covariates and their confidence levels. The study also shows how this effect changes with the ranges of the covariates and the sample size. There is qualitative agreement between these study results and the corresponding expressions in the large-sample limit for the ordinary linear models. Confounding of covariates in an overfitted model (with covariates encompassing more than just the causal variables) does not bias the estimated coefficients but reduces their significance. The effect of model underfit (with some causal variables excluded as covariates) or misfit (with covariates encompassing only noncausal variables), on the other hand, leads to not only erroneous estimated coefficients, but a misguided confidence, represented by large t-values, that the estimated coefficients are significant. The results of this study indicate that models which use only one or two air quality variables, such as particulate matter [less than and equal to] 10 microm and sulfur dioxide, are probably unreliable, and that models containing several correlated and toxic or potentially toxic air quality variables should also be investigated in order to minimize the situation of model underfit or misfit.  (+info)

Wavelet transform to quantify heart rate variability and to assess its instantaneous changes. (7/9497)

Heart rate variability is a recognized parameter for assessing autonomous nervous system activity. Fourier transform, the most commonly used method to analyze variability, does not offer an easy assessment of its dynamics because of limitations inherent in its stationary hypothesis. Conversely, wavelet transform allows analysis of nonstationary signals. We compared the respective yields of Fourier and wavelet transforms in analyzing heart rate variability during dynamic changes in autonomous nervous system balance induced by atropine and propranolol. Fourier and wavelet transforms were applied to sequences of heart rate intervals in six subjects receiving increasing doses of atropine and propranolol. At the lowest doses of atropine administered, heart rate variability increased, followed by a progressive decrease with higher doses. With the first dose of propranolol, there was a significant increase in heart rate variability, which progressively disappeared after the last dose. Wavelet transform gave significantly better quantitative analysis of heart rate variability than did Fourier transform during autonomous nervous system adaptations induced by both agents and provided novel temporally localized information.  (+info)

Excess of high activity monoamine oxidase A gene promoter alleles in female patients with panic disorder. (8/9497)

A genetic contribution to the pathogenesis of panic disorder has been demonstrated by clinical genetic studies. Molecular genetic studies have focused on candidate genes suggested by the molecular mechanisms implied in the action of drugs utilized for therapy or in challenge tests. One class of drugs effective in the treatment of panic disorder is represented by monoamine oxidase A inhibitors. Therefore, the monoamine oxidase A gene on chromosome X is a prime candidate gene. In the present study we investigated a novel repeat polymorphism in the promoter of the monoamine oxidase A gene for association with panic disorder in two independent samples (German sample, n = 80; Italian sample, n = 129). Two alleles (3 and 4 repeats) were most common and constituted >97% of the observed alleles. Functional characterization in a luciferase assay demonstrated that the longer alleles (3a, 4 and 5) were more active than allele 3. Among females of both the German and the Italian samples of panic disorder patients (combined, n = 209) the longer alleles (3a, 4 and 5) were significantly more frequent than among females of the corresponding control samples (combined, n = 190, chi2 = 10.27, df = 1, P = 0.001). Together with the observation that inhibition of monoamine oxidase A is clinically effective in the treatment of panic disorder these findings suggest that increased monoamine oxidase A activity is a risk factor for panic disorder in female patients.  (+info)

Identifying Unusual Observations in Ridge Regression Linear Model Using Box-Cox Power Transformation TechniqueIdentifying Unusual Observations in Ridge Regression Linear Model Using Box-Cox Power Transformation Technique
The use of [1] Box-Cox power transformation in regression analysis is now common; in the last two decades there has been emphasis on diagnostics methods for Box-Cox power transformation, much of which has involved deletion of influential data cases. The pioneer work of [2] studied local influence on constant variance perturbation in the Box-Cox unbiased regression linear mode. Tsai and Wu [3] analyzed local influence method of [2] to assess the effect of the case-weights perturbation on the transformation-power estimator in the Box-Cox unbiased regression linear model. Many authors noted that the influential observations on the biased estimators are different from the unbiased estimators. In this paper I describe a diagnostic method for assessing the local influence on the constant variance perturbation on the transformation in the Box-Cox biased ridge regression linear model. Two real macroeconomic data sets are used to illustrate the methodologies.
A posteriori error estimates for viscous flow problems with rotation<...A posteriori error estimates for viscous flow problems with rotation<...
TY - JOUR. T1 - A posteriori error estimates for viscous flow problems with rotation. AU - Gorshkova, E.. AU - Mahalov, Alex. AU - Neittaanmäki, P.. AU - Repin, S.. PY - 2007/4/1. Y1 - 2007/4/1. N2 - A new functional type a posteriori error estimates for the Stokes problem with rotating term are presented. The estimates give guaranteed upper bounds for the energy norm of the error and provide reliable error indication. Computational properties of the estimates are demonstrated by a number of numerical examples. Bibliography: 37 titles.. AB - A new functional type a posteriori error estimates for the Stokes problem with rotating term are presented. The estimates give guaranteed upper bounds for the energy norm of the error and provide reliable error indication. Computational properties of the estimates are demonstrated by a number of numerical examples. Bibliography: 37 titles.. UR - http://www.scopus.com/inward/record.url?scp=33846975805&partnerID=8YFLogxK. UR - ...
Statistical Estimation - An Introduction - Statistical Estimation | CourseraStatistical Estimation - An Introduction - Statistical Estimation | Coursera
Video created by University of London for the course Statistics for International Business. For statistical analysis to work properly, its essential to have a proper sample, drawn from a population of items of interest that have measured ...
Estimating the distribution of times from HIV seroconversion to aids using multiple imputation<...Estimating the distribution of times from HIV seroconversion to aids using multiple imputation<...
TY - JOUR. T1 - Estimating the distribution of times from HIV seroconversion to aids using multiple imputation. AU - Taylor, Jeremy M.G.. AU - Muñoz, Alvaro. AU - Bass, Sue M.. AU - Saah, Alfred J.. AU - Chmiel, Joan S.. AU - Kingsley, Lawrence A.. PY - 1990/5. Y1 - 1990/5. N2 - Multiple imputation is a model based technique for handling missing data problems. In this application we use the technique to estimate the distribution of times from HIV seroconversion to AIDS diagnosis with data from a cohort study of 4954 homosexual men with 4 years of follow‐up. In this example the missing data are the dates of diagnosis with AIDS. The imputation procedure is performed in two stages. In the first stage, we estimate the residual AIDS‐free time distribution as a function of covariates measured on the study participants with data provided by the participants who were seropositive at study entry, Specifically, we assume the residual AIDS‐free times follow a log‐normal regression model that ...
Statistical analysis and handling of missing data in cluster randomized trials: A systematic review<...Statistical analysis and handling of missing data in cluster randomized trials: A systematic review<...
TY - JOUR. T1 - Statistical analysis and handling of missing data in cluster randomized trials. T2 - A systematic review. AU - Fiero, Mallorie H.. AU - Huang, Shuang. AU - Oren, Eyal -. AU - Bell, Melanie L. PY - 2016/2/9. Y1 - 2016/2/9. N2 - Background: Cluster randomized trials (CRTs) randomize participants in groups, rather than as individuals and are key tools used to assess interventions in health research where treatment contamination is likely or if individual randomization is not feasible. Two potential major pitfalls exist regarding CRTs, namely handling missing data and not accounting for clustering in the primary analysis. The aim of this review was to evaluate approaches for handling missing data and statistical analysis with respect to the primary outcome in CRTs. Methods: We systematically searched for CRTs published between August 2013 and July 2014 using PubMed, Web of Science, and PsycINFO. For each trial, two independent reviewers assessed the extent of the missing data and ...
Data interpretation Jobs - Data interpretation Job Vacancy - Monstergulf.comData interpretation Jobs - Data interpretation Job Vacancy - Monstergulf.com
Apply to 34 Data interpretation Jobs on Monstergulf.com, UAEs Best Online Job Portal. Find Latest Data interpretation Job vacancies for Freshers & Experienced across Top Companies.
Uncategorized | Statistical HorizonsUncategorized | Statistical Horizons
I thought I knew what it meant for data to be missing at random. After all, Ive written a book titled Missing Data, and Ive been teaching courses on missing data for more than 15 years. I really ought to know what missing at random means.. But now that Im in the process of revising that book, Ive come to the conclusion that missing at random (MAR) is more complicated than I thought. In fact, the MAR assumption has some peculiar features that make me wonder if it can ever be truly satisfied in common situations when more than one variable has missing data. First, a little background. There are two modern methods for handling missing data that have achieved widespread popularity: maximum likelihood and multiple imputation. As implemented in most software packages, both of these methods depend on the assumption that the data are missing at random.. Heres how I described the MAR assumption in my book:. Data on Y are said to be missing at random if the probability of missing data on Y is ...
A Posteriori Error Estimate for Streamline Diffusion Method in Soving a Hyperbolic EquationA Posteriori Error Estimate for Streamline Diffusion Method in Soving a Hyperbolic Equation
In this article, we use streamline diffusion method for the linear second order hyperbolic initial-boundary value problem. More specifically, we prove a posteriori error estimates for this method for the linear wave equation. We observe that this error estimates make finite element method increasingly powerful rather than other methods.
Some benefits of dichotomization in psychiatric and criminological research - PDFSome benefits of dichotomization in psychiatric and criminological research - PDF
100 Criminal Behaviour and Mental Health, 10, Whurr Publishers Ltd Some benefits of dichotomization in psychiatric and criminological research DAVID P. FARRINGTON 1 and ROLF LOEBER 2 1 Institute
CiteSeerX - Citation Query A Bilinear Approach to the Parameter Estimation of a general Heteroscedastic Linear System with...CiteSeerX - Citation Query A Bilinear Approach to the Parameter Estimation of a general Heteroscedastic Linear System with...
CiteSeerX - Scientific documents that cite the following paper: A Bilinear Approach to the Parameter Estimation of a general Heteroscedastic Linear System with Application to Conic Fitting
SLE clinical trials: Impact of missing data on estimating treatment effects<...SLE clinical trials: Impact of missing data on estimating treatment effects<...
TY - JOUR. T1 - SLE clinical trials. T2 - Impact of missing data on estimating treatment effects. AU - Kim, Mimi. AU - Merrill, Joan T.. AU - Wang, Cuiling. AU - Viswanathan, Shankar. AU - Kalunian, Ken. AU - Hanrahan, Leslie. AU - Izmirly, Peter. PY - 2019/10/1. Y1 - 2019/10/1. N2 - Objective A common problem in clinical trials is missing data due to participant dropout and loss to follow-up, an issue which continues to receive considerable attention in the clinical research community. Our objective was to examine and compare current and alternative methods for handling missing data in SLE trials with a particular focus on multiple imputation, a flexible technique that has been applied in different disease settings but not to address missing data in the primary outcome of an SLE trial. Methods Data on 279 patients with SLE randomised to standard of care (SoC) and also receiving mycophenolate mofetil (MMF), azathioprine or methotrexate were obtained from the Lupus Foundation of ...
Is Multiple Imputation Possible in the Context of Survival Analysis? - The Analysis FactorIs Multiple Imputation Possible in the Context of Survival Analysis? - The Analysis Factor
Sure. One of the big advantages of multiple imputation is that you can use it for any analysis.. Its one of the reasons big data libraries use it-no matter how researchers are using the data, the missing data is handled the same, and handled well.. I say this with two caveats.. 1. One of the steps of multiple imputation is to combine the analysis results from the multiple data sets. This is very easy for parameter estimates, but its a big ugly formula for standard errors. Any software that does multiple imputation should do this combination for you. So, even if its theoretically possible, not all software will combine the results easily for you for all analyses.. 2. Censoring, which is related to missing data, but not the same, is common in survival analysis. You wouldnt want to multiply impute the censored data that occurs naturally in the survival analysis. Survival analysis has already come up with very good solutions to ...
Simulation Study for Non-Bayesian Multiple Imputation.Simulation Study for Non-Bayesian Multiple Imputation.
Rubin (1987)s combination formula for variance estimation in multiple imputation (MI) requires a imputation method to be Bayesian-proper. However, many census bureau have heavily relied on non-Bayesian imputations. Bjørnstad (2007) suggested an inflated factor (k1) in Rubin (1987)s combination formula for non-Bayesian imputations. This paper aimed to verify the theoretical derivation of Bjørnstad (2007) in computer simulation. Within Bjørnstad (2007)s pre-assumed environment, the inflated factor, k1, closely approached the simulated true value, E(k), irrespective of sample size and missing rate. With California schools data, confidence intervals using k1 also achieved a desired coverage, (1-a)%, across varying sample size and missing rate, except in case of MNAR because of biased imputation ...
Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls | The BMJMultiple imputation for missing data in epidemiological and clinical research: potential and pitfalls | The BMJ
A variety of ad hoc approaches are commonly used to deal with missing data. These include replacing missing values with values imputed from the observed data (for example, the mean of the observed values), using a missing category indicator,7 and replacing missing values with the last measured value (last value carried forward).8 None of these approaches is statistically valid in general, and they can lead to serious bias. Single imputation of missing values usually causes standard errors to be too small, since it fails to account for the fact that we are uncertain about the missing values.. When there are missing outcome data in a randomised controlled trial, a common sensitivity analysis is to explore "best" and "worst" case scenarios by replacing missing values with "good" outcomes in one group and "bad" outcomes in the other group. This can be useful if there are only a few missing values of a binary outcome, but because imputing all missing values to good or bad is a strong assumption the ...
Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls | The BMJMultiple imputation for missing data in epidemiological and clinical research: potential and pitfalls | The BMJ
A variety of ad hoc approaches are commonly used to deal with missing data. These include replacing missing values with values imputed from the observed data (for example, the mean of the observed values), using a missing category indicator,7 and replacing missing values with the last measured value (last value carried forward).8 None of these approaches is statistically valid in general, and they can lead to serious bias. Single imputation of missing values usually causes standard errors to be too small, since it fails to account for the fact that we are uncertain about the missing values.. When there are missing outcome data in a randomised controlled trial, a common sensitivity analysis is to explore "best" and "worst" case scenarios by replacing missing values with "good" outcomes in one group and "bad" outcomes in the other group. This can be useful if there are only a few missing values of a binary outcome, but because imputing all missing values to good or bad is a strong assumption the ...
Learning From Data: Semiparametric Models Versus Faith-based... : EpidemiologyLearning From Data: Semiparametric Models Versus Faith-based... : Epidemiology
We appreciate the thoughtful comments by Subramanian and OMalley1 to our paper2 on comparing mixed models and population average models, and the opportunity this response affords us to make a stronger and more general case regarding prevalent misconceptions surrounding statistical estimation. There are several technical points made in the paper that can be debated, but we will focus on what we believe is the crux of their critique-an issue that is widely shared (either explicitly or implicitly) by analyses of a majority of researchers using statistical inference from data to support scientific hypotheses.. We start with what we hope is an accurate summary of their argument: nonparametric identifiability of a parameter of interest from the observed data, considering knowledge available on the data-generating distribution, should not be a major concern in deciding on the choice of parameter of interest within a chosen data-generating model. Instead, the scientific question should guide the types ...
Find Others Other Banks Bank Specialist Officer Recruitment Data Interpretation Practice Tests 2017: Complete Study MaterialFind Others Other Banks Bank Specialist Officer Recruitment Data Interpretation Practice Tests 2017: Complete Study Material
Others Other Banks Bank Specialist Officer Recruitment Data Interpretation Practice Tests 2017: Find on Jagran Josh Bank Exam Test Prep Center. Get Free Study Material for All Bank Exams.
Solas - Statistical Software for Missing ValuesSolas - Statistical Software for Missing Values
Solas is a user-friendly application for missing value imputation. Solas provides a large pool of imputation methods for missing values.
Example 44.5 GEE for Binary Data with Logit Link Function :: SAS/STAT(R) 14.1 Users GuideExample 44.5 GEE for Binary Data with Logit Link Function :: SAS/STAT(R) 14.1 User's Guide
0 would be modeled by default. Information about the GEE model is displayed in Output 44.5.2. The results of GEE model fitting are displayed in Output 44.5.3. Model goodness-of-fit criteria are displayed in Output 44.5.4. If you specify no other options, the standard errors, confidence intervals, Z scores, and p-values are based on empirical standard error estimates. You can specify the MODELSE option in the REPEATED statement to create a table based on model-based standard error estimates. ...
Advanced Data Transformation Solutions | Informatica USAdvanced Data Transformation Solutions | Informatica US
Advanced Data Transformation is a comprehensive, enterprise-class data transformation solution for any data type, regardless of format or complexity.
Bootstrap Methods: A Guide for Practitioners and Researchers, 2nd Edition | Applied Probability & Statistics | General &...Bootstrap Methods: A Guide for Practitioners and Researchers, 2nd Edition | Applied Probability & Statistics | General &...
A practical and accessible introduction to the bootstrap method--newly revised and updated Over the past decade, the application of bootstrap methods to new areas of study has expanded, resulting in theoretical and applied advances across various fields. Bootstrap Methods, Second Edition is a highly approachable guide to the multidisciplinary, real-world uses of bootstrapping and is ideal for readers who have a professional interest in its methods, but are without an advanced background in mathematics.. Updated to reflect current techniques and the most up-to-date work on the topic, the Second Edition features:. ...
Example 63.15 Creating Control-Based Pattern Imputation in Sensitivity Analysis :: SAS/STAT(R) 13.2 Users GuideExample 63.15 Creating Control-Based Pattern Imputation in Sensitivity Analysis :: SAS/STAT(R) 13.2 User's Guide
values in the treatment group is similar to the corresponding distribution of individuals in the control group. Ratitch and OKelly (2011) describe an implementation of the pattern-mixture model approach that uses a control-based pattern imputation. That is, an imputation model for the missing observations in the treatment group is constructed not from the observed data in the treatment group but rather from the observed data in the control group. This model is also the imputation model that is used to impute missing observations in the control group. Table 63.10 shows the variables in the data set. For the control-based pattern imputation, all missing ...
Elon University - STS256Elon University - STS256
This course focuses on data-oriented approaches to statistical estimation and inference using techniques that do not depend on the distribution of the variable(s) being assessed. Topics include classical rank-based methods, as well as modern tools such as permutation tests and bootstrap methods. Advanced statistical software such as SAS or SPlus may be used, and written reports will link statistical theory and practice with communication of results.. ...
RNA-seqlopediaRNA-seqlopedia
As with any experiment that is intended to test a null hypothesis of no difference between or among groups of individuals, differential expression studies using RNA-seq data need to be replicated in order to estimate within- and among-group variation. We understand that constraints in some study systems make replication very difficult, but it really is important. Statistical hypothesis tests are prone to two types of error. Failure to reject the null hypothesis of no difference when there actually is a difference (a "false negative") is known as type II error, and β is used to symbolize the probability of its occurrence. The number of replicates per group in an experiment directly affects type II error, and therefore "statistical power" (which is 1-β). Power also depends on the magnitude of the effect of one condition relative to another on the variable of interest, which is in part determined by the degree of variation among individuals. Thirdly, power depends on the acceptable maximum ...
CRAN - Package SurrogateTestCRAN - Package SurrogateTest
Provides functions to test for a treatment effect in terms of the difference in survival between a treatment group and a control group using surrogate marker information obtained at some early time point in a time-to-event outcome setting. Nonparametric kernel estimation is used to estimate the test statistic and perturbation resampling is used for variance estimation. More details will be available in the future in: Parast L, Cai T, Tian L (2017) "Using a Surrogate Marker for Early Testing of a Treatment Effect" (under review).. ...
The Multilevel Approach to Repeated Measures for Complete and Incomplete Data, Quality & Quantity | 10.1023/A:1022545930672 |...The Multilevel Approach to Repeated Measures for Complete and Incomplete Data, Quality & Quantity | 10.1023/A:1022545930672 |...
Read "The Multilevel Approach to Repeated Measures for Complete and Incomplete Data, Quality & Quantity" on DeepDyve, the largest online rental service for scholarly research with thousands of academic publications available at your fingertips.
Difference between revisions of Probability Seminar - UW-Math WikiDifference between revisions of "Probability Seminar" - UW-Math Wiki
Title: Quantitative CLTs for random walks in random environments Abstract:The classical central limit theorem (CLT) states that for sums of a large number of i.i.d. random variables with finite variance, the distribution of the rescaled sum is approximately Gaussian. However, the statement of the central limit theorem doesnt give any quantitative error estimates for this approximation. Under slightly stronger moment assumptions, quantitative bounds for the CLT are given by the Berry-Esseen estimates. In this talk we will consider similar questions for CLTs for random walks in random environments (RWRE). That is, for certain models of RWRE it is known that the position of the random walk has a Gaussian limiting distribution, and we obtain quantitative error estimates on the rate of convergence to the Gaussian distribution for such RWRE. This talk is based on joint works with Sungwon Ahn and Xiaoqin Guo. ...
Difference between revisions of Probability Seminar - UW-Math WikiDifference between revisions of "Probability Seminar" - UW-Math Wiki
Title: Quantitative CLTs for random walks in random environments Abstract:The classical central limit theorem (CLT) states that for sums of a large number of i.i.d. random variables with finite variance, the distribution of the rescaled sum is approximately Gaussian. However, the statement of the central limit theorem doesnt give any quantitative error estimates for this approximation. Under slightly stronger moment assumptions, quantitative bounds for the CLT are given by the Berry-Esseen estimates. In this talk we will consider similar questions for CLTs for random walks in random environments (RWRE). That is, for certain models of RWRE it is known that the position of the random walk has a Gaussian limiting distribution, and we obtain quantitative error estimates on the rate of convergence to the Gaussian distribution for such RWRE. This talk is based on joint works with Sungwon Ahn and Xiaoqin Guo. ...
Uncategorized - Statistical DataUncategorized - Statistical Data
Statistics is data collection in order to later organize, analyse, interpret and also present them in a specific manner that gives inside look in the problem and probable solutions in the area that is being studied. It can be used in many spheres from science to social and industrial fields. One of the most prominent hypotheses that is used very often in statistics in the null hypothesis, because in this discipline in many cases the null hypothesis is assumed true until evidence proves otherwise.. The null hypothesis in general is a statement or default positions that suggests that between two specific measures phenomena there is no relationships. Therefore with the help of statistics researcher need to determine that there is a relationship between two phenomena in order to disprove the null hypothesis.. The null hypothesis also know as ad denoted as H0 is used in two very different statistical approaches. In the first approach called significant testing that was patented by Roland Fisher the ...
Multiple Imputation: 5 Recent Findings that Change How to Use ItMultiple Imputation: 5 Recent Findings that Change How to Use It
Missing Data, and multiple imputation specifically, is one area of statistics that is changing rapidly. Research is still ongoing, and each year new findings on best practices and new techniques in software appear. The downside for researchers is that some
Meeting the challenge of the Psychonomic Societys 2012 Guidelines on Statistical Issues: Some success and some room for...Meeting the challenge of the Psychonomic Society's 2012 Guidelines on Statistical Issues: Some success and some room for...
The Psychonomic Society (PS) ado pted New Statistical Guidelines for Journals of the Psychonomic Society in November 2012. To evaluate changes in statistical re porting within and outside PS journals,
Meeting the challenge of the Psychonomic Societys 2012 Guidelines on Statistical Issues: Some success and some room for...Meeting the challenge of the Psychonomic Society's 2012 Guidelines on Statistical Issues: Some success and some room for...
The Psychonomic Society (PS) ado pted New Statistical Guidelines for Journals of the Psychonomic Society in November 2012. To evaluate changes in statistical re porting within and outside PS journals,
Statistical ProceduresStatistical Procedures
Describe the correct statistical procedures for analysis for this question: How satisfied are users of the XYZ program with the service they have received? Include reference and page.
Recent Articles | Data Interpretation, Cell & Molecular Biology And Culture | The Scientist Magazine®Recent Articles | Data Interpretation, Cell & Molecular Biology And Culture | The Scientist Magazine®
In chapter 3, "The Sense of Sensibility," author Wendy Jones uses scenes from one of Jane Austens most celebrated novels to illustrate the functioning of the bodys stress response system.. 0 Comments. ...
PSY K300 25006 Statistical TechniquesPSY K300 25006 Statistical Techniques
Introduction to statistics; nature of statistical data; ordering and manipulation of data; measures of central tendency and dispersion; elementary probability. Concepts of statistical inference and decision: estimation and hypothesis testing. Special topics include regression and correlation, and analysis of variance ...
New Statistical Methods for Demographic Data Analysis - Grace WahbaNew Statistical Methods for Demographic Data Analysis - Grace Wahba
This research is for the development of new approaches to the analysis of data from large cohort studies, either epidemiologic or clinical trials, with many qua...
NHANES Tutorials - Software TipsNHANES Tutorials - Software Tips
The degrees of freedom associated with an estimated statistic is needed to perform hypothesis tests and to compute confidence intervals. For analyses on a subgroup of the NHANES population, the degrees of freedom should be based on the number of strata and PSUs containing the observations of interest. Stata procedures generally calculate the degrees of freedom based on the number of strata and PSUs represented in the overall dataset. Estimates for some subgroups of interest will have fewer degrees of freedom than are available in the overall analytic dataset. (See Module 4: Variance Estimation for more information.). In particular, although the ...
Basic descriptive and inferrential statistical data analysis using StataBasic descriptive and inferrential statistical data analysis using Stata
The main objective of this workshop is to equip students, researchers and staff involved in carrying out and supervising quantitative research, with the necessary skills to perform basic analysis of categorical and continuous quantitiatve data using Stata. This will be achieved by providing practical instruction and facilitated exercises in ...
Department of PsychologyDepartment of Psychology
Ng, V. K. & Cribbie, R.A. (in press). The gamma generalized linear model, log transformation, and the robust Yuen-Welch test for analyzing group means with skewed and heteroscedastic data. Communications in Statistics: Simulation and Computation. ...
Analyzing Quantitative Data: An Introduction for Social Researchers | Quality Productivity & Reliability | General &...Analyzing Quantitative Data: An Introduction for Social Researchers | Quality' Productivity & Reliability | General &...
Preface xiii Part I. Summarizing Data 1. 1. Data Organization 3. 1.1 Introduction 3. 1.2 Consideration of Variables 4. 1.3 Coding 15. 1.4 Data Manipulations 18. 1.5 Conclusion 20. 2. Descriptive Statistics for Categorical Data 33. 2.1 Introduction 33. 2.2 Frequency Tables 35. 2.3 Crosstabulations 37. 2.4 Graphs and Charts 45. 2.5 Conclusion 50. 3. Descriptive Statistics for Continuous Data 63. 3.1 Introduction 63. 3.2 Frequencies 64. 3.3 Measures of Central Tendency 70. 3.4 Measures of Dispersion 73. 3.5 Standardized Scores 79. 3.6 Conclusion 88. Part II. Statistical Tests 101. 4. Evaluating Statistical Significance 103. 4.1 Introduction 103. 4.2 Central Limit Theorem 104. 4.3 Statistical Significance 107. 4.4 The Roles of Hypotheses 115. 4.5 Conclusion 119. 5. The Chi-Square Test: Comparing Category Frequencies 125. 5.1 Introduction 125. 5.2 The Chi-Square Distribution 126. 5.3 Performing Chi-Square Tests 130. 5.4 Post Hoc Testing 143. 5.5 Confidence Intervals 146. 5.6 Explaining Results of the ...
P-values of 308 gene sets in the p53 data analysis: p-v | Open-iP-values of 308 gene sets in the p53 data analysis: p-v | Open-i
P-values of 308 gene sets in the p53 data analysis: p-values of Global Test and ANCOVA Global Test after standardization vs. SAM-GS p-values before the standard
Log transformation and multiple imputation | Statistics Help @ Talk Stats ForumLog transformation and multiple imputation | Statistics Help @ Talk Stats Forum
Hello, below is a part of an assignment. Can someone tell me whether I have to perform log transformation before or after multiply imputing the data...
Victor Cruz, Sterling Shepard went missing at worst time | New York PostVictor Cruz, Sterling Shepard went missing at worst time | New York Post
GREEN BAY, Wis. - While Odell Beckham Jr. is seen as the star, Victor Cruz and rookie Sterling Shepard are the other two usually formidable links of the...
Suppose we wish to perform a two-sample test, but... - OpenStudySuppose we wish to perform a two-sample test, but... - OpenStudy
Suppose we wish to perform a two-sample test, but we do not want to make any normality (or other strong parametric) assumptions. Conduct an appropria...
GraphPad - repeated measures non parametric test with missing values - alternative? | Statistics Help @ Talk Stats ForumGraphPad - repeated measures non parametric test with missing values - alternative? | Statistics Help @ Talk Stats Forum
I am trying to compare a matched cohort of repeated measures - within each treatment arm with 5 time points comparing to baseline. The normality test is...
SketchMorphSketchMorph
forwardDirection is the angle at which the object will go forward. When the rotationStyle is not #normal, then forwardDirection is any angle, while the rotation is highly restricted. If flexed, this is remembered by the Transform morph. For non-normal rotationStyle, it is rotationDegrees ...
Two-Sample Z-test OutputTwo-Sample Z-test Output
For a 2-tailed test, the p-value represents the probability of making a type 1 error (concluding there is statistical significance when there is none). Since there is far less than a 5% chance of making a type 1 error, you would conclude there is statistical significance ...
Statistical analysis plan (SAP)Statistical analysis plan (SAP)
According to ICH guidelines a Statistical Analysis Plan should be prepared prior to unblinding the clinical study. The aim of the Statistical Analysis Plan is to minimise bias by clearly stating the proposed methods of dealing with protocol deviators, early withdrawals, missing data, and the way(s) in which anticipated analysis problems will be handled as well as many other possible issues.. The Statistical Analysis Plan will usually include sample layouts for tables and listings to be produced. Therefore preparation of a Statistical Analysis Plan is a key component in the conduct of a rigorous clinical trial and requires a statistician with both formal statistical training and significant experience in the pharmaceutical industry.. At Statistical Revelations we have experienced statisticians who have been involved in the preparation of Statistical Analysis Plans in most therapeutic areas and all phases of clinical research.. ...
ASPPH | Arizona Reviews Statistical Analysis and Handling of Missing Data in Cluster Randomized TrialsASPPH | Arizona Reviews Statistical Analysis and Handling of Missing Data in Cluster Randomized Trials
Cluster randomized trials (CRTs) randomize participants in groups, rather than as individuals and are key tools used to assess interventions in health research where treatment contamination is likely or if individual randomization is not feasible. Two potential major pitfalls exist regarding CRTs, namely handling missing data and not accounting for clustering in the primary analysis.. Ms. Mallorie Fiero, a doctoral student in biostatistics at the University of Arizona Mel and Enid Zuckerman College of Public Health and colleagues reviewed approaches for handling missing data and statistical analysis with respect to the primary outcome in CRTs. The study was published in the journal Trials.. The investigators systematically searched for CRTs published between August 2013 and July 2014 using PubMed, Web of Science, and PsycINFO. For each trial, two independent reviewers assessed the extent of the missing data and method(s) used for handling missing data in the primary and sensitivity analyses. ...
Nonparametric lack-of-fit tests in presence of heteroscedastic variancesNonparametric lack-of-fit tests in presence of heteroscedastic variances
It is essential to test the adequacy of a specified regression model in order to have cor- rect statistical inferences. In addition, ignoring the presence of heteroscedastic errors of regression models will lead to unreliable and misleading inferences. In this dissertation, we consider nonparametric lack-of-fit tests in presence of heteroscedastic variances. First, we consider testing the constant regression null hypothesis based on a test statistic constructed using a k-nearest neighbor augmentation. Then a lack-of-fit test of nonlinear regression null hypothesis is proposed. For both cases, the asymptotic distribution of the test statistic is derived under the null and local alternatives for the case of using fixed number of nearest neighbors. Numerical studies and real data analyses are presented to evaluate the perfor- mance of the proposed tests. Advantages of our tests compared to classical methods include: (1) The response variable can be discrete or continuous and can have variations ...
A posteriori error estimates for sequential laminates in shape optimizationA posteriori error estimates for sequential laminates in shape optimization
A posteriori error estimates are derived in the context of two-dimensional structural elastic shape optimization under the compliance objective. It is known that the optimal shape features are microstructures that can be constructed using sequential lamination. The descriptive parameters explicitly depend on the stress. To derive error estimates the dual weighted residual approach for control problems in PDE constrained optimization is employed, involving the elastic solution and the microstructure parameters. Rigorous estimation of interpolation errors ensures robustness of the estimates while local approximations are used to obtain fully practical error indicators. Numerical results show sharply resolved interfaces between regions of full and intermediate material density.
SP0188 Multiple Imputation: A Possible Solution To The Problem of Missing Data | Annals of the Rheumatic DiseasesSP0188 Multiple Imputation: A Possible Solution To The Problem of Missing Data | Annals of the Rheumatic Diseases
Multiple imputation (MI) is a statistical technique that can be used to handle the problem of missing data. MI enables the use of all the available data without throwing any away and can avoid the bias and unrealistic estimates of uncertainty associated with other methods for handling missing data. In MI, the missing values in the data are filled in or "imputed" by sampling from distributions observed in the available data. This sampling is done multiple times, resulting in multiple datasets. Each of the multiple datasets is analysed and the results are combined to give overall results which reflect the uncertainty about the values of the missing data. This talk will explore what MI is, when it can be used and how to use it. The content will be accessible to a wide audience and illustrated with clear examples. ...
Robust linear static panel data models using ε-contamination | Centre de recherche sur les risques, les enjeux économiques et...Robust linear static panel data models using ε-contamination | Centre de recherche sur les risques, les enjeux économiques et...
The paper develops a general Bayesian framework for robust linear static panel data models using ε-contamination. A two-step approach is employed to derive the conditional type-II maximum likelihood (ML-II) posterior distribution of the coeffcients and individual effects. The ML-II posterior densities are weighted averages of the Bayes estimator under a base prior and the data-dependent empirical Bayes estimator. Two-stage and three stage hierarchy estimators are developed and their finite sample performance is investigated through a series of Monte Carlo experiments. These include standard random effects as well as Mundlak-type, Chamberlain-type and Hausman-Taylor-type models. The simulation results underscore the relatively good performance of the three-stage hierarchy estimator. Within a single theoretical framework, our Bayesian approach encompasses a variety of specifications while conventional methods require separate estimators for each case.. ...
st: Placebo Multiple Imputation...st: Placebo Multiple Imputation...
Id like to run a special sort of conditional multiple imputation algorithm whereby the imputation model/algorithm is based purely on the data from the placebo arm of a trial and then using this created algorithm impute missing values not just for the placebo group but also for the treated group as well. It does not look like this is possible with conditional multiple imputation routine in Stata 12. Can anyone please suggest a way of doing this - fancy code, existing ado or maybe possible in Stata 13? Many thanks, Steve STEVE KAY , DIRECTOR OF STATISTICS & HEOR MODELLING , McCANN COMPLETE MEDICAL This email may contain confidential or legally privileged information, intended only for the addressee. If you have received this email in error, you are hereby notified that any disclosure, copying, distribution or reliance upon the contents of this email is strictly prohibited. Please contact the sender to arrange for correct delivery, and then delete this email. Any views or opinions presented in ...
Chapter 12. Variance Estimation - Design and Methodology: American Community Survey - Survey ACS 2010 (5-Year Estimates) -...Chapter 12. Variance Estimation - Design and Methodology: American Community Survey - Survey ACS 2010 (5-Year Estimates) -...
Data Documentation - Survey ACS 2010 (5-Year Estimates); Design and Methodology: American Community Survey; Chapter 12. Variance Estimation
Multivariate Stochastic Volatility with Co-HeteroscedasticityMultivariate Stochastic Volatility with Co-Heteroscedasticity
Downloadable! This paper develops a new methodology that decomposes shocks into homoscedastic and heteroscedastic components. This specification implies there exist linear combinations of heteroscedastic variables that eliminate heteroscedasticity. That is, these linear combinations are homoscedastic; a property we call co-heteroscedasticity. The heteroscedastic part of the model uses a multivariate stochastic volatility inverse Wishart process. The resulting model is invariant to the ordering of the variables, which we show is important for impulse response analysis but is generally important for, e.g., volatility estimation and variance decompositions. The specification allows estimation in moderately high-dimensions. The computational strategy uses a novel particle filter algorithm, a reparameterization that substantially improves algorithmic convergence and an alternating-order particle Gibbs that reduces the amount of particles needed for accurate estimation. We provide two empirical applications;
The Dantzig selector: Statistical estimation when p is much larger than n | Whats newThe Dantzig selector: Statistical estimation when p is much larger than n | What's new
I dont think you should jump from "X is colinear" to "estimation of β is essentially hopeless". It depends on the loss function.. Consider the changepoint problem. A piecewise constant vector Y is equal to Lβ, where L is a lower triangular matrix of 1s and β is sparse. In the presence of noise you cant find an estimate β* which will perfectly recover β. But you consider it a job well-done if the non-zero entries of β* are near the non-zero entries of β.. This suggests a loss function something like. $$\sum_{i=1}^p (\beta^*_i - a_i)^2 + \,\beta\,_0$$. where. $$a_i = \frac{1}{11}\sum_{k=i-5}^{i+5} \beta_i$$. This problem has a sequential structure, and there are similar problems with more complex structures. For example, heres a similar problem with a tree structure. You are given a phylogenetic tree of $n$ species, and for each species $i$, you are given $y_i$, the copy number of a certain gene in the genome of that species. Where, on the phylogenetic tree, did this gene undergo ...
Stata Bookstore: Applied Longitudinal Data Analysis for Epidemiology: A Practical Guide, Second EditionStata Bookstore: Applied Longitudinal Data Analysis for Epidemiology: A Practical Guide, Second Edition
Applied Longitudinal Data Analysis for Epidemiology: A Practical Guide, Second Edition, by Jos W. R. Twisk provides a practical introduction to the estimation techniques used by epidemiologists for longitudinal data.
Income missing values imputation: EVS 1999 and 2008Income missing values imputation: EVS 1999 and 2008
Downloadable! Missing data is a very frequent obstacle in many social science studies. The absence of values on one or more variables can signi?cantly affect statistical analyses by reducing their precision and by introducing selection biases. Being unable to account for these aspects may result in severe misrepresentation of the phenomenon under analysis. For this reason several approaches have been proposed to impute missing values. In present work I will adopt multiple imputation to impute income missing data for Luxembourg in the European Values Study data-set of 1999 and 2008.
Robust regression analysis for non-normal situations under symmetric distributions arising in medical research<...Robust regression analysis for non-normal situations under symmetric distributions arising in medical research<...
TY - JOUR. T1 - Robust regression analysis for non-normal situations under symmetric distributions arising in medical research. AU - Ganguly, S. S.. PY - 2014. Y1 - 2014. N2 - In medical research, while carrying out regression analysis, it is usually assumed that the independent (covariates) and dependent (response) variables follow a multivariate normal distribution. In some situations, the covariates may not have normal distribution and instead may have some symmetric distribution. In such a situation, the estimation of the regression parameters using Tikus Modified Maximum Likelihood (MML) method may be more appropriate. The method of estimating the parameters is discussed and the applications of the method are illustrated using real sets of data from the field of public health.. AB - In medical research, while carrying out regression analysis, it is usually assumed that the independent (covariates) and dependent (response) variables follow a multivariate normal distribution. In some ...
Confidence Intervals Following Box-Cox Transformation by P. M. Hooper and Zhenlin YANG"Confidence Intervals Following Box-Cox Transformation" by P. M. Hooper and Zhenlin YANG
What is the interpretation of a confidence interval following estimation of a Box-Cox transformation parameter ?? Several authors have argued that confidence intervals for linear model parameters ? can be constructed as if ? were known in advance, rather than estimated, provided the estimand is interpreted conditionally given ??. If the estimand is defined as ? (??), a function of the estimated transformation, can the nominal confidence level be regarded as a conditional coverage probability given ??, where the interval is random and the estimand is fixed? Or should it be regarded as an unconditional probability, where both the interval and the estimand are random? This article investigates these questions via large-n approximations, small-? approximations, and simulations. It is shown that, when model assumptions are satisfied and n is large, the nominal confidence level closely approximates the conditional coverage probability. When n is small, this conditional approximation is still good for
Contest Underway to Clarify Best Practices for Genomic Data Interpretation | GenomeWebContest Underway to Clarify Best Practices for Genomic Data Interpretation | GenomeWeb
NEW YORK (GenomeWeb News) - An array of contestants are participating in a contest to decode the DNA sequences of three children with rare diseases in order to establish best practices for genomic data interpretation, the contests organizers announced this week.
CiteSeerX - Citation Query Adjusting for Nonignorable Drop-Out Using Semiparametric Nonresponse Models (with discussionCiteSeerX - Citation Query Adjusting for Nonignorable Drop-Out Using Semiparametric Nonresponse Models (with discussion
CiteSeerX - Scientific documents that cite the following paper: Adjusting for Nonignorable Drop-Out Using Semiparametric Nonresponse Models (with discussion
Matillion ETL for Azure Synapse to Enable Data Transformations - Database Trends and ApplicationsMatillion ETL for Azure Synapse to Enable Data Transformations - Database Trends and Applications
Matillion, a provider of data transformation software for cloud data warehouses (CDWs), is releasing Matillion ETL for Azure Synapse to enable data transformations in complex IT environments, at scale. Empowering enterprises to achieve faster time to insights by loading, transforming, and joining together data, the release extends Matillions product portfolio to further serve Microsoft Azure customers.
Analysis of Testing-Based Forward Model Selection | The Econometric SocietyAnalysis of Testing-Based Forward Model Selection | The Econometric Society
p. 2147-2173. Damian Kozbur This paper analyzes a procedure called Testing‐Based Forward Model Selection (TBFMS) in linear regression problems. This procedure inductively selects covariates that add predictive power into a working statistical model before estimating a final regression. The criterion for deciding which covariate to include next and when to stop including covariates is derived from a profile of traditional statistical hypothesis tests. This paper proves probabilistic bounds, which depend on the quality of the tests, for prediction error and the number of selected covariates. As an example, the bounds are then specialized to a case with heteroscedastic data, with tests constructed with the help of Huber-Eicker-White standard errors. Under the assumed regularity conditions, these tests lead to estimation convergence rates matching other common high‐dimensional estimators including Lasso.. ...
CDA1 - Categorical Data Analysis Independent(Explanatory Variable is Categorical(Nominal or Ordinal Dependent(Response Variable...CDA1 - Categorical Data Analysis Independent(Explanatory Variable is Categorical(Nominal or Ordinal Dependent(Response Variable...
View Notes - CDA1 from STA 6934 at University of Florida. Categorical Data Analysis Independent (Explanatory) Variable is Categorical (Nominal or Ordinal) Dependent (Response) Variable
Statistical Analysis Plan (SAP) | The Parker InstituteStatistical Analysis Plan (SAP) | The Parker Institute
The Parker Institute · Copenhagen University Hospital, Bispebjerg og Frederiksberg · Nordre Fasanvej 57 · Road 8, entrance 19 · DK-2000 Frederiksberg ...
Significance analysis of microarrays applied to the ionizing radiation response | PNASSignificance analysis of microarrays applied to the ionizing radiation response | PNAS
SAM is a method for identifying genes on a microarray with statistically significant changes in expression, developed in the context of an actual biological experiment. SAM was successful in analyzing this experiment as well as several other experiments with oligonucleotide and cDNA microarrays (data not shown).. In the statistics of multiple testing (28-30), the family-wise error rate (FWER) is the probability of at least one false positive over the collection of tests. The Bonferroni method, the most basic method for bounding the FWER, assumes independence of the different tests. An acceptable FWER could be achieved for our microarray data only if the corresponding threshold was set so high that no genes were identified. The step-down correction method of Westfall and Young (29), adapted for microarrays by Dudoit et al. (http://www.stat.berkeley.edu/users/terry/zarray/Html/matt.html), allows for dependent tests but still remains too stringent, yielding no genes from our data.. Westfall and ...
Data Interpretation Shortcuts : Techniques to Solve Quickly | Bank Exams TodayData Interpretation Shortcuts : Techniques to Solve Quickly | Bank Exams Today
1. With D.I section, one can test the aspirant ability to solve statistical data. 2. In Banking Industry, there is demand of those who are highly proficient in calculation. This is because bank employees need to work on statistical data on daily basis.
IASRI Library catalog › Details for: Methods for statistical data analysis of multivariate observationsIASRI Library catalog › Details for: Methods for statistical data analysis of multivariate observations
BookSeries: Wiley Series in Probability and Mathematical Statistics. Publisher: New York John Wiley and sons 1977Description: 311p.ISBN: 9780471308454.Subject(s): Mathematics , Multivariate Analysis , Statistical Methods , Statistical data analysis ...
The AstroStat Slog » Blog Archive » From Quantile Probability and Statistical Data ModelingThe AstroStat Slog » Blog Archive » From Quantile Probability and Statistical Data Modeling
I teach that statistics (done the quantile way) can be simultaneously frequentist and Bayesian, confidence intervals and credible intervals, parametric and nonparametric, continuous and discrete data. My first step in data modeling is identification of parametric models; if they do not fit, we provide nonparametric models for fitting and simulating the data. The practice of statistics, and the modeling (mining) of data, can be elegant and provide intellectual and sensual pleasure. Fitting distributions to data is an important industry in which statisticians are not yet vendors. We believe that unifications of statistical methods can enable us to advertise, "What is your question? Statisticians have answers!" ...
Biases in comparative analyses of extinction risk: mind the gap - González-Suárez - 2012 - Journal of Animal Ecology - Wiley...Biases in comparative analyses of extinction risk: mind the gap - González-Suárez - 2012 - Journal of Animal Ecology - Wiley...
We have identified important data biases in the mammalian life-history literature, which appear to reflect a pattern of data not missing at random. That is, the probability of not having information for a trait depends on the unobserved values of that trait (Little & Rubin 2002). This presents a great challenge for analysing these data because as we have seen here deleting species with missing data greatly reduces the available sample size and introduces biases in model estimates. However, conventional techniques to fill gaps (such as multiple imputation) generally assume that data are missing at random or completely at random (Little & Rubin 2002; Nakagawa & Freckleton 2008). For data not missing at random, it is possible to use imputation but a clear understanding of the mechanism causing the missing data is generally necessary. However, missing data in PanTHERIA are likely missing as a result of multiple mechanisms. For example, some species may be harder to study because of their life ...
BigData: Probabilistic Methods for Efficient Search and Statistical Learning in Extremely High-Dimensional Data | Texas ECEBigData: Probabilistic Methods for Efficient Search and Statistical Learning in Extremely High-Dimensional Data | Texas ECE
This talk will present a series of work on probabilistic hashing methods which typically transform a challenging (or infeasible) massive data computational problem into a probability and statistical estimation problem. For example, fitting a logistic regression (or SVM) model on a dataset with billion observations and billion (or billion square) variables would be difficult. Searching for similar documents (or images) in a repository of billion web pages (or images) is another challenging example.
lect04 - Categorical Data Analysis Lei Sun 1 CHL 5210 Statistical Analysis of Qualitative Data Topic Logistic Regression...lect04 - Categorical Data Analysis Lei Sun 1 CHL 5210 Statistical Analysis of Qualitative Data Topic Logistic Regression...
View Notes - lect04 from CHL 5210H at University of Toronto. Categorical Data Analysis - Lei Sun 1 CHL 5210 - Statistical Analysis of Qualitative Data Topic: Logistic Regression Outline • Single
Journal of Data Science: Volume 7, Number 2, April 2009Journal of Data Science: Volume 7, Number 2, April 2009
Simultaneous tests of a huge number of hypotheses is a core issue in high flow experimental methods such as microarray for transcriptomic data. In the central debate about the type I error rate, Benjamini and Hochberg (1995) have proposed a procedure that is shown to control the now popular False Discovery Rate (FDR) under assumption of independence between the test statistics. These results have been extended to a larger class of dependency by Benjamini and Yekutieli (2001) and improvements have emerged in recent years, among which step-up procedures have shown desirable properties. The present paper focuses on the type II error rate. The proposed method improves the power by means of double-sampling test statistics integrating external information available both on the sample for which the outcomes are measured and also on additional items. The small sample distribution of the test statistics is provided and simulation studies are used to show the beneficial impact of introducing relevant ...
Methods for Statistical and Visual Comparison of Imputation Methods for Missing Data in Software Cost Estimation: Computer...Methods for Statistical and Visual Comparison of Imputation Methods for Missing Data in Software Cost Estimation: Computer...
Methods for Statistical and Visual Comparison of Imputation Methods for Missing Data in Software Cost Estimation: 10.4018/978-1-60960-215-4.ch009: Software Cost Estimation is a critical phase in the development of a software project, and over the years has become an emerging research area. A common
Protecting against nonrandomly missing data in longitudinal studies<...Protecting against nonrandomly missing data in longitudinal studies<...
TY - JOUR. T1 - Protecting against nonrandomly missing data in longitudinal studies. AU - Brown, C. H.. PY - 1990/7/25. Y1 - 1990/7/25. N2 - Nonrandomly missing data can pose serious problems in longitudinal studies. We generally have little knowledge about how missingness is related to the data values, and longitudinal studies are often far from complete. Two approaches that have been used to handle missing data-use of maximum likelihood with an ignorable mechanism and direct modeling of the missing data mechanism-have the disadvantage of not giving consistent estimates under important classes of nonrandom mechanisms. We introduce two protective estimators, that is, estimators that retain their consistency over a wide range of nonrandom mechanisms. We compare these protective estimators using longitudinal data from a mental health panel study. We also investigate their robustness to certain departures from normality.. AB - Nonrandomly missing data can pose serious problems in longitudinal ...
Lecture: Heteroskedasticity, Weighted Least Squares, and Variance Estimation (Advanced Data Analysis from an Elementary Point...Lecture: Heteroskedasticity, Weighted Least Squares, and Variance Estimation (Advanced Data Analysis from an Elementary Point...
Weighted least squares estimates, to give more emphasis to particular data points. Heteroskedasticity and the problems it causes for inference. How weighted least squares gets around the problems of heteroskedasticity, if we know the variance function. Estimating the variance function from regression residuals. An iterative method for estimating the regression function and the variance function together. Locally constant and locally linear modeling. Lowess. Reading: Notes, chapter 7 ...
Univariate Analysis and Normality Test Using SAS, Stata, and SPSSUnivariate Analysis and Normality Test Using SAS, Stata, and SPSS
Descriptive statistics provide important information about variables to be analyzed. Mean, median, and mode measure central tendency of a variable. Measures of dispersion include variance, standard deviation, range, and interquantile range (IQR). Researchers may draw a histogram, stem-and-leaf plot, or box plot to see how a variable is distributed. Statistical methods are based on various underlying assumptions. One common assumption is that a random variable is normally distributed. In many statistical analyses, normality is often conveniently assumed without any empirical evidence or test. But normality is critical in many statistical methods. When this assumption is violated, interpretation and inference may not be reliable or valid. The t-test and ANOVA (Analysis of Variance) compare group means, assuming a variable of interest follows a normal probability distribution. Otherwise, these methods do not make much sense. Figure 1 illustrates the standard normal probability distribution and a ...
sem - Regression analysis with factor scores as explanatory variable - Cross Validatedsem - Regression analysis with factor scores as explanatory variable - Cross Validated
Structural equation modeling may be the appropriate method. It tends to be most useful and valid when you have multiple links that you want to identify in a causal chain; when multivariate normality is present; when any missing data are missing completely at random; when N is fairly large; and (I think) when variables are measured without much error. Absent such conditions, exploratory factor analysis scores may be quite useful as regression predictors, assuming the EFA (as well as the regression) is done in a sound, thoughtful way. A lot of people make the mistake of treating EFA as a routinized procedure, as you can read about in the wonderful article Repairing Tom Swifts Electric Factor Analysis Machine. EFA involves many decision points and few iron-clad guidelines for them. 42.2% of all EFA solutions that I run across smack of what I believe to be significant errors in choice of extraction method, number of factors to extract, inclusion/exclusion of variables, or others.. ...
Analysis of Randomly Incomplete Data Wi... | WHSmith BooksAnalysis of Randomly Incomplete Data Wi... | WHSmith Books
Buy Analysis of Randomly Incomplete Data Without Imputation (SpringerBriefs in Statistics 2012) by Tejas Desai From WHSmith today! FREE delivery to stor...
Statistical & Data Analysis Software Package | MinitabStatistical & Data Analysis Software Package | Minitab
Unlock the value of your data with Minitab Statistical Software. Drive cost containment, improve quality & increase effectiveness through data analysis.
Big Data and Spurious Correlations - Practical Statistical Inference | CourseraBig Data and Spurious Correlations - Practical Statistical Inference | Coursera
Video created by University of Washington for the course Practical Predictive Analytics: Models and Methods. Learn the basics of statistical inference, comparing classical methods with resampling methods that allow you to use a simple program ...
9780521573917: Bootstrap Methods and their Application (Cambridge Series in Statistical and Probabilistic Mathematics) -...9780521573917: Bootstrap Methods and their Application (Cambridge Series in Statistical and Probabilistic Mathematics) -...
Bootstrap Methods and their Application (Cambridge Series in Statistical and Probabilistic Mathematics) de A. C. Davison; D. V. Hinkley en Iberlibro.com - ISBN 10: 0521573912 - ISBN 13: 9780521573917 - Cambridge University Press - 1997 - Tapa dura
The two-stage design in a non-stringent test situation. | Open-iThe two-stage design in a non-stringent test situation. | Open-i
The two-stage design in a non-stringent test situation. (A) Data simulation experiment: empirical density functions of the DE genes (solid curve), noisy non-DE
Lecture 5C Part 1 Time to Event Data: Graphical Summarization: Kapalan-Meier Approach - Module 2C: Summarization and...Lecture 5C Part 1 Time to Event Data: Graphical Summarization: Kapalan-Meier Approach - Module 2C: Summarization and...
Video created by Johns Hopkins University for the course Statistical Reasoning for Public Health 1: Estimation, Inference, & Interpretation. This module consists of a single lecture set on time-to-event outcomes. Time-to-event data comes ...
Statistical MethodologyStatistical Methodology
After 33 volumes, Statistical Methodology will be discontinued as of 31st December 2016. At this point the possibility to submit manuscripts has been...
Dworsky: Probably Not: Future Prediction Using Probability and Statistical Inference, 2nd Edition - Student Companion SiteDworsky: Probably Not: Future Prediction Using Probability and Statistical Inference, 2nd Edition - Student Companion Site
Welcome to the Web site for Probably Not: Future Prediction Using Probability and Statistical Inference, 2nd Edition by Lawrence N. Dworsky. This Web site gives you access to the rich tools and resources available for this text. You can access these resources in two ways ...
Recent Articles | Data Interpretation, Ecology And Developmental Biology | The Scientist Magazine®Recent Articles | Data Interpretation, Ecology And Developmental Biology | The Scientist Magazine®
Daily News Thousands of Mutations Accumulate in the Human Brain Over a Lifetime Single-cell genome analyses reveal the amount of mutations a human brain cell will collect from its fetal beginnings until death.. ...
Browsing Faculty and Researcher Data and Papers by Subject Data Interpretation, StatisticalBrowsing Faculty and Researcher Data and Papers by Subject "Data Interpretation, Statistical"
The purpose of this work was 2-fold. First, we sought to develop statistical criteria by which it could be established that the coincident occurrence of pulses of two different hormones exceeds that which ...
Adjustment of an Intensive Care Unit (ICU) Data in Fuzzy C-Regression Models | Journal of Science and TechnologyAdjustment of an Intensive Care Unit (ICU) Data in Fuzzy C-Regression Models | Journal of Science and Technology
This research is an attempt to present a proper methodology in data modification by using analytical hierarchy process (AHP) technique and fuzzy c-mean (FCM) model. The continuous data were built from binary data using analytical hierarchy process (AHP). Whereas, the binary data were created from continuous data using fuzzy cmeans (FCM) model. The models used in this research are fuzzy c-regression models (FCRM). A case study in scale of health at an intensive care unit (ICU) ward using the AHP, FCM model and FCRM models was carried out. There are six independent variables involved in this study. There are four cases considered as a result of using AHP technique and FCM model toward independent data. After comparing the four cases, it was found that case 4 appeared to be the best model, having the lowest mean square error (MSE). The original data have the MSE value of 97.33, while the data of case 4 have MSE by 83.48. This means that the AHP technique can lower the MSE, while the FCM model ...
Preprocessing data for tabular built-in algorithmsPreprocessing data for tabular built-in algorithms
There are further differences in the transformation process, depending on which ML framework the built-in algorithm is based on. For the TensorFlow-based built-in algorithms (linear learner, wide and deep), the column treatments correspond directly to which feature columns are created in the TensorFlow model. AI Platform Training simply assigns feature columns for the TensorFlow Estimator model, and then the data transformation becomes part of the preprocessing that occurs within the TensorFlow Estimator model.. Otherwise, as with XGBoost, AI Platform Training applies column treatments and performs the data transformations directly.. ...
9780023556500: Probability and statistical inference - AbeBooks - Robert V. Hogg; Elliot A. Tanis: 00235565019780023556500: Probability and statistical inference - AbeBooks - Robert V. Hogg; Elliot A. Tanis: 0023556501
AbeBooks.com: Probability and statistical inference (9780023556500) by Robert V. Hogg; Elliot A. Tanis and a great selection of similar New, Used and Collectible Books available now at great prices.
The Skeptical EnvironmentalistThe Skeptical Environmentalist
List of important publications in computer scienceList of important publications in computer science
Level of measurementLevel of measurement
Linear trend estimationLinear trend estimation
Laboratory quality controlLaboratory quality control
Level of measurementLevel of measurement
Computational immunologyComputational immunology
Quantitative marketing researchQuantitative marketing research
Protocol (science)Protocol (science)
List of fields of application of statisticsList of fields of application of statistics
D. J. BartholomewD. J. Bartholomew
Peter RousseeuwPeter Rousseeuw
Misuse of statisticsMisuse of statistics
Historical revisionismHistorical revisionism
Conceptual modelConceptual model
Alice S. WhittemoreAlice S. Whittemore
HD 187085 bHD 187085 b
Likelihood-ratio testLikelihood-ratio test
Institute of Psychiatry, Psychology and NeuroscienceInstitute of Psychiatry, Psychology and Neuroscience
Mind projection fallacyMind projection fallacy
Population declinePopulation decline
Psychometric softwarePsychometric software
Lists of mathematics topicsLists of mathematics topics
Mark BoguskiMark Boguski
Irving GottesmanIrving Gottesman
Tolerance intervalTolerance interval
Spray characteristicsSpray characteristics
Sanjay SubrahmanyamSanjay Subrahmanyam
Longevity mythsLongevity myths
Comparative rating index of sovereignsComparative rating index of sovereigns
2000 United States Census2000 United States Census
Level of measurementLevel of measurement
Mann-Whitney U testMann-Whitney U test
DowsingDowsing
Brain tumorBrain tumor
Refractive indexRefractive index
Observer effect (physics)Observer effect (physics)
Occupational health psychologyOccupational health psychology
Free willFree will
Daniel DennettDaniel Dennett
Myers-Briggs Type IndicatorMyers-Briggs Type Indicator
Ole RømerOle Rømer
Islamic calendarIslamic calendar
Robert KatesRobert Kates
NOMINATE (scaling method)NOMINATE (scaling method)
ChemistryChemistry
List of atheists in science and technologyList of atheists in science and technology
Age of the universeAge of the universe
Abortion in the United StatesAbortion in the United States
EpistasisEpistasis
Indigenous AustraliansIndigenous Australians
Romani peopleRomani people
Hard disk driveHard disk drive
Grounded theoryGrounded theory
Ras manusia bahasa Indonesia, ensiklopedia bebasRas manusia bahasa Indonesia, ensiklopedia bebas
Hebrew languageHebrew language
B. R. AmbedkarB. R. Ambedkar
Sleep disorderSleep disorder
Ashkenazi JewsAshkenazi Jews
DenialismDenialism
AnatomyDiseasesChemicals and DrugsAnalytical, Diagnostic and Therapeutic Techniques and EquipmentPsychiatry and PsychologyPhenomena and ProcessesDisciplines and OccupationsAnthropology, Education, Sociology and Social PhenomenaTechnology, Industry, AgricultureInformation ScienceHealth Care
Data Interpretation, StatisticalPlant BarkReproducibility of ResultsSoftwareOligonucleotide Array Sequence AnalysisAlgorithmsComputer SimulationGene Expression ProfilingComputational BiologySequence Analysis, DNAPolymerase Chain ReactionSensitivity and SpecificityObserver VariationImage Interpretation, Computer-AssistedRadiologyTime FactorsDiagnostic ErrorsModels, BiologicalEquipment DesignMaps as TopicConfidence IntervalsDevelopmental DisabilitiesProbabilityModels, StatisticalToxapheneArthritis, PsoriaticArthritis, RheumatoidArthritisMethotrexateArthritis, JuvenileArthritis, ExperimentalWetlandsLakesPsychology, AppliedOrthodontic Appliance DesignFresh WaterDecision MakingResearchForensic GeneticsGenetics, PopulationCriminologyForensic MedicineForensic AnthropologyY ChromosomeNational Institutes of Health (U.S.)Climatic ProcessesHydrologyCyclonic StormsUncertaintyModels, TheoreticalTidal Volume
Statistical Interpretation of Toxicity Data | SpringerLinkStatistical Interpretation of Toxicity Data | SpringerLink
ASQ/ANSI/ISO 16269-7:2001: Statistical interpretation of data - Part 7: Median - Estimation and confidence intervals | ASQASQ/ANSI/ISO 16269-7:2001: Statistical interpretation of data - Part 7: Median - Estimation and confidence intervals | ASQ
2020 RFA for Great Lakes Coastal Wetland Monitoring Program | Great Lakes Funding | US EPA2020 RFA for Great Lakes Coastal Wetland Monitoring Program | Great Lakes Funding | US EPA
Frontiers | Meta-Analysis of Global Transcriptomics Suggests that Conserved Genetic Pathways are Responsible for Quercetin and...Frontiers | Meta-Analysis of Global Transcriptomics Suggests that Conserved Genetic Pathways are Responsible for Quercetin and...
WHO HQ Library catalog › Results of search for su:{Data interpretation, Statistical.}WHO HQ Library catalog › Results of search for 'su:{Data interpretation, Statistical.}'
Difference between revisions of Reduction of uncertainties through Data Model Integration (DMI) - MarineSpecies Introduced...Difference between revisions of "Reduction of uncertainties through Data Model Integration (DMI)" - MarineSpecies Introduced...
Statistical analyses of multidomain data for the microbiome | Microsoft Research | Channel 9Statistical analyses of multidomain data for the microbiome | Microsoft Research | Channel 9
CiteSeerX - Search Results - Kernel Sparse Subspace Clustering with a Spatial Max Pooling Operation for Hyperspectral Remote...CiteSeerX - Search Results - Kernel Sparse Subspace Clustering with a Spatial Max Pooling Operation for Hyperspectral Remote...
May 3, 2018 - OC Weekly by Duncan McIntosh Company - IssuuMay 3, 2018 - OC Weekly by Duncan McIntosh Company - Issuu
Re: st: GLS interpretationRe: st: GLS interpretation
Re: st: Logistic regression interpretationRe: st: Logistic regression interpretation
Relationship Between Abdominal Symptoms and Fructose Ingestion in Children with Chronic Abdominal Pain | SpringerLinkRelationship Between Abdominal Symptoms and Fructose Ingestion in Children with Chronic Abdominal Pain | SpringerLink
Randomised, double blind, placebo controlled crossover trial of sustained release morphine for the management of refractory...Randomised, double blind, placebo controlled crossover trial of sustained release morphine for the management of refractory...
CiteSeerX - Making the most of statistical analyses: Improving interpretation and presentationCiteSeerX - Making the most of statistical analyses: Improving interpretation and presentation
Exploring Spatial Differences Between 2 US Firearm Mortality Data Sets in 2017Exploring Spatial Differences Between 2 US Firearm Mortality Data Sets in 2017
Bayesian interpretation of kernel regularization - WikipediaBayesian interpretation of kernel regularization - Wikipedia
ISO - ISO/TC 69 - Applications of statistical methodsISO - ISO/TC 69 - Applications of statistical methods
Statistical Analysis facts, information, pictures | Encyclopedia.com articles about Statistical AnalysisStatistical Analysis facts, information, pictures | Encyclopedia.com articles about Statistical Analysis
Hematopoietic Npc1 mutation shifts gut microbiota composition in Ldlr −/− mice on a high-fat, high-cholesterol diet |...Hematopoietic Npc1 mutation shifts gut microbiota composition in Ldlr −/− mice on a high-fat, high-cholesterol diet |...
Oxford handbook of public health practice. (eBook, 2013) [WorldCat.org]Oxford handbook of public health practice. (eBook, 2013) [WorldCat.org]
Industry sponsorship and research outcomeIndustry sponsorship and research outcome
A weighted generalized score statistic for comparison of predictive values of diagnostic testsA weighted generalized score statistic for comparison of predictive values of diagnostic tests
National Highway Runoff Water-Quality Data and Methodology Synthesis (NDAMS)National Highway Runoff Water-Quality Data and Methodology Synthesis (NDAMS)
Variation in the relative copy number of the TLR7 gene in patients with systemic lupus erythematosus and healthy control...Variation in the relative copy number of the TLR7 gene in patients with systemic lupus erythematosus and healthy control...
Statistical interpretation of medical data of patients with alcohol abuse - Open Medicine - Volume 7, Issue 4 (2012) - PSJD -...Statistical interpretation of medical data of patients with alcohol abuse - Open Medicine - Volume 7, Issue 4 (2012) - PSJD -...
Design, Analysis, and Interpretation of Genome-Wide Association Scans | Ebook | Ellibs EbookstoreDesign, Analysis, and Interpretation of Genome-Wide Association Scans | Ebook | Ellibs Ebookstore
Research Designs in Applied Health - University of LeicesterResearch Designs in Applied Health - University of Leicester
Log-Linear Modeling: Concepts, Interpretation, and Application | Categorical Data Analysis | Data Analysis | General &...Log-Linear Modeling: Concepts, Interpretation, and Application | Categorical Data Analysis | Data Analysis | General &...
Post Mortem Radiology for Natural and Forensic Death Investigation, Short course - one module | University of LeicesterPost Mortem Radiology for Natural and Forensic Death Investigation, Short course - one module | University of Leicester
AnatomyDiseasesChemicals and DrugsAnalytical, Diagnostic and Therapeutic Techniques and EquipmentPsychiatry and PsychologyPhenomena and ProcessesDisciplines and OccupationsAnthropology, Education, Sociology and Social PhenomenaTechnology, Industry, AgricultureInformation ScienceHealth Care
Data Interpretation, StatisticalPlant BarkReproducibility of ResultsSoftwareOligonucleotide Array Sequence AnalysisAlgorithmsComputer SimulationGene Expression ProfilingComputational BiologySequence Analysis, DNAPolymerase Chain ReactionSensitivity and SpecificityObserver VariationImage Interpretation, Computer-AssistedRadiologyTime FactorsDiagnostic ErrorsModels, BiologicalEquipment DesignMaps as TopicConfidence IntervalsDevelopmental DisabilitiesProbabilityModels, StatisticalToxapheneArthritis, PsoriaticArthritis, RheumatoidArthritisMethotrexateArthritis, JuvenileArthritis, ExperimentalWetlandsLakesPsychology, AppliedOrthodontic Appliance DesignFresh WaterDecision MakingResearchForensic GeneticsGenetics, PopulationCriminologyForensic MedicineForensic AnthropologyY ChromosomeNational Institutes of Health (U.S.)Climatic ProcessesHydrologyCyclonic StormsUncertaintyModels, TheoreticalTidal Volume
InferenceAnalysesHypothesisMethodsAnalysis and interpretation of dataConfidence intervalsRegressionMultivariateAnalyticalActuarial SciencesEmpiricalMethodologicalLaboratoryConclusionsAcquisitionBiological dataAppropriate statisticalMethodologyAnalytics2017CollectionExploratoryMathematicalInterpretDichotomous dataResults of statistical analysisSPSSPracticeConceptsBayesianProbabilityConductResearchSamplesEpidemiologicComputeCritical revisionPredictiveRange of statisticalCommonlyStudyParametersResearchersTechniquesDescriptorEmphasisAssessmentMeasurementPresenting DataSubjectsQualitativePracticalMedianTestsClinical2000OrdinalExperimental dataReporting
Inference6
Analyses7
Hypothesis2
Methods20
Analysis and interpretation of data1
Confidence intervals1
Regression1
Multivariate1
  • The intelligent data analysis on the clinical parameter dataset has shown that when a complex system is considered as a multivariate one, the information about the system substantially increases. (edu.pl)
Analytical2
Actuarial Sciences1
Empirical4
  • The existing statistical tests for testing equality of predictive values are either Wald tests based on the multinomial distribution or the empirical Wald and generalized score tests within the generalized estimating equations (GEE) framework. (nih.gov)
  • To alleviate this, we introduce a weighted generalized score (WGS) test statistic that incorporates empirical covariance matrix with newly proposed weights. (nih.gov)
  • It also serves as an excellent reference for applied researchers in virtually any area of study, from medicine and statistics to the social sciences, who analyze empirical data in their everyday work. (wiley.com)
  • Graduates holding a master's degree in quantitative psychology may analyze empirical data obtained from scientific research and/or conduct scientific research on psychometrics or statistical phenomena. (mtsu.edu)
Methodological2
Laboratory2
Conclusions9
Acquisition6
Biological data1
Appropriate statistical2
Methodology4
Analytics3
  • This course teaches how to explore data, build reports & queries using SAS Visual Analytics. (sas.com)
  • He has conducted mentoring workshops in the area of Business Forecasting, Predictive Modeling, Big Data Analytics, Operations Research & Design of Experiments with leading corporations in the area of banking, retail, manufacturing & agriculture. (sas.com)
  • He is responsible for building the analytics group at the small, rapidly growing biotech company in Marietta, Ga. The coursework, faculty, and student interactions at MTSU provided a strong foundation that enabled Tucker to have many early career successes in the healthcare field, particularly in using data for complex decision-making, he says. (mtsu.edu)
20173
  • Spatial distribution of unadjusted death rate per 100,000 population of deaths from gun violence across the contiguous United States, by state, in 2017 in 2 data sets: the Centers for Disease Control and Prevention (CDC) Wide-ranging OnLine Data for Epidemiologic Research (WONDER) database (1) (panel 1a) and the Gun Violence Archive (GVA) (2) (panel 1b). (cdc.gov)
  • Cluster maps of the spatial dependency of gun violence mortality rates across the contiguous United States, by state, in 2017 in 2 data sets: the Centers for Disease Control and Prevention (CDC) Wide-ranging OnLine Data for Epidemiologic Research (WONDER) database (1) (panel 2a) and the Gun Violence Archive (GVA) (2) (panel 2b). (cdc.gov)
  • Through this project, we analyzed spatial similarities in 2017 between the CDC WONDER and GVA data sets for the contiguous United States. (cdc.gov)
Collection8
  • Applicants are expected to manage data generated through sample collection and submit to EPA. (epa.gov)
  • For comparison, we used the GVA, an independent data collection and research group that collects data on gun violence deaths and injuries from law enforcement, media, and commercial sources with the goal of providing near real-time gun violence data (2). (cdc.gov)
  • It is necessary to establish systematic data-quality objectives, an integrated quality system, and standard protocols for sample collection, processing, analysis, documentation, and publication to ensure that resources expended to meet environmental research needs are used ef?ciently and effectively. (usgs.gov)
  • Integration of Federal, State, and local regulatory, data-collection, and research programs within a system to facilitate information transfer will provide an economy of scale by making research results available to the entire research community. (usgs.gov)
  • I believe that this meeting is an important step for forest data collection in the Pacific. (fao.org)
  • I note from the agenda that there will be presentations from the countries on the status and level of their data collection and papers on specific topics presented by the different resource persons. (fao.org)
  • I believe this workshop is important to the overall management of the region s forests, is timely, and will go far in addressing the region s data collection problems. (fao.org)
  • Many think that data collection is as simple as asking the respondents a few questions about a survey. (teach-nology.com)
Exploratory1
Mathematical4
Interpret1
Dichotomous data1
  • thus dichotomous data involves the construction of classifications as well as the classification of items. (wikipedia.org)
Results of statistical analysis2
SPSS2
  • I have some data here that need to be done via spss . (freelancer.com)
  • I have learned not only the mathematics and conceptual ideas behind a number of advanced statistical techniques but I have also gained experience with writing syntax in the statistical programs, SAS, SPSS, and R," Freund says. (mtsu.edu)
Practice3
  • The focus is also on practice in oral presentations, discussion and written interpretations of primary literature. (bowdoin.edu)
  • That is far from actual statistical practice. (teach-nology.com)
  • We know that very few students today go to work in state and local health agencies and, in the absence of accurate data that would track where our graduates go to work, SPH need to train emerging health professionals to practice in every imaginable worksite. (nap.edu)
Concepts3
Bayesian1
Probability1
Conduct2
Research14
  • Statistical operations : analysis of health research data / Robert P. Hirsch, Richard K. Riegelman. (who.int)
  • The most commonly used source for research on gun-related deaths is the Centers for Disease Control and Prevention (CDC) Wide-ranging OnLine Data for Epidemiologic Research (WONDER) database, which compiles data from death certificates (1). (cdc.gov)
  • Results of the metadata-review process indicate that few reports document enough of the information and data necessary to establish the quality or representativeness of research results. (usgs.gov)
  • Her research focuses on extending generalized latent variable modeling to the study of clustered, repeated measures longitudinal data. (wiley.com)
  • As attached to the China Center of IUE Satellite Data, the Center seeks to promote significant advances and research in these fields. (caltech.edu)
  • Conducting the statistical analysis for a research project. (uwo.ca)
  • The R software ecosystem is currently the most widely used platform for advanced, data-analytic research across all disciplines. (warwick.ac.uk)
  • To give participants an opportunity to introduce, discuss with fellow participants and experts, and solve, specific data-analytic issues arising in their own research. (warwick.ac.uk)
  • Reproducibility is a key challenge for data-intensive research, and this course will provide useful tools and techniques to help ensure that research results are robust and replicable by others. (warwick.ac.uk)
  • This course provides fundamental tools and understanding for data-based research. (warwick.ac.uk)
  • Skills in data analysis, and especially in R , are highly marketable outside academia (for example in business and government, all the way to journalism) as well as in academic research. (warwick.ac.uk)
  • The AUA understands that statistical analysis is a key component of health care research, which is why it offers comprehensive professional data analysis to urologists and urology practices, other researchers and government and industry groups. (auanet.org)
  • The quantitative psychology master's program aims to prepare students for applied and research careers as statisticians, psychometricians, data analysts, and quantitative psychologists in education, business, government, and other organizations. (mtsu.edu)
  • He has stated that he began his research as an attempt to counter what he saw as anti-ecological arguments by Julian Lincoln Simon in an article in Wired, but changed his mind after starting to analyze data. (wikipedia.org)
Samples6
Epidemiologic1
Compute2
Critical revision1
Predictive1
  • Thus, we believe that the proposed WGS statistic is the preferred statistic for testing equality of two predictive values and for corresponding sample size computations. (nih.gov)
Range of statistical1
Commonly3
  • And finally, they wanted to generate more sophisticated interpretations of DNA STR profiles (the characterizations found in CODIS) than are commonly used. (ojp.gov)
  • They also investigated the mathematics of commonly used statistical calculations and the effects of further complications, such as the presence of family members in DNA sample mixtures. (ojp.gov)
  • Although CDC WONDER is the most commonly used source for data on gun violence, concerns have been voiced around the validity of cause-of-death reporting on death certificates (5,6). (cdc.gov)
Study6
  • Application of statistical procedures to analyze specific observed or assumed facts from a particular study. (rush.edu)
  • Applicants must specify in their application the process by which additional coastal wetland sites/studies will be identified and incorporated into the monitoring program while not comprising overall study objectives and data quality. (epa.gov)
  • The current study focuses on CVD risk factors using nationally representative data to determine prevalence of biological CVD risk factors (prehypertension/hypertension, borderline-high/high LDL-C, low HDL-C, and prediabetes/diabetes) by weight status (normal weight, overweight, obese) and their trends among US adolescents aged 12 to 19 years. (aappublications.org)
  • Data were derived from 1351 subjects, aged 18-69 years and enrolled in the ORISCAV-LUX study. (mdpi.com)
  • The 126-item MiniMeal-Q, developed after the validation study, was evaluated in a simulated validation by using truncated Meal-Q data. (jmir.org)
  • The challenge with these data is that the time to the outcome is fully observed on some subjects, but not on those who do not have the outcome during their tenure in the study. (coursera.org)
Parameters4
  • Cluster analysis and principal components analysis were applied on the above 21 biochemical parameters data. (edu.pl)
  • The main goal is to identify the data set structure, finding groups of similarity among the clinical parameters or among the patients. (edu.pl)
  • An effort is made to connect the role of kinetic parameters for acetaldehyde metabolism with biochemical, ethanol kinetic and anthropometric data in parallel. (edu.pl)
  • Another nice feature of the KZ filter is that the two parameters have clear interpretation so that it can be easily adopted by specialists in different areas. (wikipedia.org)
Researchers2
Techniques5
Descriptor1
Emphasis2
Assessment3
  • An assessment of the quality of data on age at first union, first birth, and first sexual intercourse for phase II of the Demographic and Health Surveys program / Anastasia J. Gage. (who.int)
  • With appropriate data and information, proper assessment can be made of the status of the different forest resources, and more importantly, enable right management decisions and practices to be made and applied. (fao.org)
  • She used real-world national data during her Discovery Education Assessment internship. (mtsu.edu)
Measurement2
Presenting Data1
Subjects1
  • Time-to-event data comes primarily from prospective cohort studies with subjects who haven to had the outcome of interest at their time of enrollment. (coursera.org)
Qualitative2
Practical1
Median1
  • On the other hand, the median , i.e. the middle-ranked item, makes no sense for the nominal type of data since ranking is meaningless for the nominal type. (wikipedia.org)
Tests4
Clinical4
  • Heo M, Kim N, Rinke ML, Wylie-Rosett J. Sample size determinations for stepped-wedge clinical trials from a three-level data hierarchy perspective. (rush.edu)
  • Limited valid data are available regarding the association of fructose-induced symptoms, fructose malabsorption, and clinical symptoms. (springer.com)
  • Some recent data projects include evaluating and analyzing the geographic distribution of physicians, determinants of hospital charges, AUA Annual Meeting attendance, member satisfaction surveys, membership trends and clinical studies on conditions such as overactive bladder (OAB), BPH, and hypogonadism. (auanet.org)
  • Despite these observational data, no large controlled clinical trials have been conducted to evaluate the relationship between the age of stored RBCs and clinical outcomes. (pnas.org)
20001
  • In 'The American Statistician' (February 2000, Vol. 54, No. 1), George Cobb commented, 'What is new and different about Ramsey and Schafer's book, what makes it a 'larger contribution,' is that it gives much more prominence to modeling and interpretation of the sort that goes beyond the routine patterns. (booktopia.com.au)
Ordinal1
  • The ordinal type allows for rank order (1st, 2nd, 3rd, etc.) by which data can be sorted, but still does not allow for relative degree of difference between them. (wikipedia.org)
Experimental data1
  • Many of the topics discussed in this chapter pertain to experimental data in general, but the context of their use and examples given are in the field of toxicology. (springer.com)
Reporting1
  • Ten papers reported on sponsorship and effect size, but could not be pooled due to differences in their reporting of data. (nih.gov)
Statistical analyses of multidomain data for the microbiome | Microsoft Research | Channel 9
Statistical analyses of multidomain data for the microbiome | Microsoft Research | Channel 9 (channel9.msdn.com)
May 3, 2018 - OC Weekly by Duncan McIntosh Company - Issuu
May 3, 2018 - OC Weekly by Duncan McIntosh Company - Issuu (issuu.com)
Relationship Between Abdominal Symptoms and Fructose Ingestion in Children with Chronic Abdominal Pain | SpringerLink
Relationship Between Abdominal Symptoms and Fructose Ingestion in Children with Chronic Abdominal Pain | SpringerLink (link.springer.com)
Randomised, double blind, placebo controlled crossover trial of sustained release morphine for the management of refractory...
Randomised, double blind, placebo controlled crossover trial of sustained release morphine for the management of refractory... (bmj.com)
CiteSeerX - Making the most of statistical analyses: Improving interpretation and presentation
CiteSeerX - Making the most of statistical analyses: Improving interpretation and presentation (citeseer.ist.psu.edu)
Exploring Spatial Differences Between 2 US Firearm Mortality Data Sets in 2017
Exploring Spatial Differences Between 2 US Firearm Mortality Data Sets in 2017 (cdc.gov)
Browsing by Subject Data Interpretation, Statistical
Browsing by Subject "Data Interpretation, Statistical" (extranet.who.int)
Hematopoietic Npc1 mutation shifts gut microbiota composition in Ldlr −/− mice on a high-fat, high-cholesterol diet |...
Hematopoietic Npc1 mutation shifts gut microbiota composition in Ldlr −/− mice on a high-fat, high-cholesterol diet |... (nature.com)
Browsing Publications by Subject Data Interpretation, Statistical
Browsing Publications by Subject "Data Interpretation, Statistical" (extranet.who.int)
The A-SACT (Achievement in Singapore of Cholesterol Targets) Study in Patients with Coronary Heart Disease | SpringerLink
The A-SACT (Achievement in Singapore of Cholesterol Targets) Study in Patients with Coronary Heart Disease | SpringerLink (link.springer.com)
Design, Analysis, and Interpretation of Genome-Wide Association Scans | Ebook | Ellibs Ebookstore
Design, Analysis, and Interpretation of Genome-Wide Association Scans | Ebook | Ellibs Ebookstore (ellibs.com)
Predictive Models for Patient-reported Spine Surgery Outcomes
Predictive Models for Patient-reported Spine Surgery Outcomes (medscape.com)
The occurrence of fibre-type grouping in healthy human muscle: a quantitative study of cross-sections of whole vastus lateralis...
The occurrence of fibre-type grouping in healthy human muscle: a quantitative study of cross-sections of whole vastus lateralis... (link.springer.com)
Industry Payments to Academic Physicians: a Comparison of Reporting to Two Government Agencies | SpringerLink
Industry Payments to Academic Physicians: a Comparison of Reporting to Two Government Agencies | SpringerLink (link.springer.com)
WDSI/IAS Vacation School 2017
WDSI/IAS Vacation School 2017 (warwick.ac.uk)
Quantifying the nonclassicality of pure dephasing | Nature Communications
Quantifying the nonclassicality of pure dephasing | Nature Communications (nature.com)