**Data Interpretation, Statistical**: Application of statistical procedures to analyze specific observed or assumed facts from a particular study.

**Algorithms**: A procedure consisting of a sequence of algebraic formulas and/or logical steps to calculate or determine a given task.

**Plant Bark**: The outer layer of the woody parts of plants.

**Software**: Sequential operating programs and data which instruct the functioning of a digital computer.

**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.

**Computer Simulation**: Computer-based representation of physical systems and phenomena such as chemical processes.

**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.

**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.

**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.

**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)

**Sequence Analysis, DNA**: A multistage process that includes cloning, physical mapping, subcloning, determination of the DNA SEQUENCE, and information analysis.

**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.

**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.

**Pattern Recognition, Automated**: In INFORMATION RETRIEVAL, machine-sensing or identification of visible patterns (shapes, forms, and configurations). (Harrod's Librarians' Glossary, 7th ed)

**Bayes Theorem**: A theorem in probability theory named for Thomas Bayes (1702-1761). In epidemiology, it is used to obtain the probability of disease in a group of people with some characteristic on the basis of the overall rate of that disease and of the likelihood of that characteristic in healthy and diseased individuals. The most familiar application is in clinical decision analysis where it is used for estimating the probability of a particular diagnosis given the appearance of some symptoms or test result.

**Artificial Intelligence**: Theory and development of COMPUTER SYSTEMS which perform tasks that normally require human intelligence. Such tasks may include speech recognition, LEARNING; VISUAL PERCEPTION; MATHEMATICAL COMPUTING; reasoning, PROBLEM SOLVING, DECISION-MAKING, and translation of language.

**Neural Networks (Computer)**: A computer architecture, implementable in either hardware or software, modeled after biological neural networks. Like the biological system in which the processing capability is a result of the interconnection strengths between arrays of nonlinear processing nodes, computerized neural networks, often called perceptrons or multilayer connectionist models, consist of neuron-like units. A homogeneous group of units makes up a layer. These networks are good at pattern recognition. They are adaptive, performing tasks by example, and thus are better for decision-making than are linear learning machines or cluster analysis. They do not require explicit programming.

**Toxicology**: The science concerned with the detection, chemical composition, and biological action of toxic substances or poisons and the treatment and prevention of toxic manifestations.

**Support Vector Machines**: Learning algorithms which are a set of related supervised computer learning methods that analyze data and recognize patterns, and used for classification and regression analysis.

**Information Storage and Retrieval**: Organized activities related to the storage, location, search, and retrieval of information.

**Databases, Factual**: Extensive collections, reputedly complete, of facts and data garnered from material of a specialized subject area and made available for analysis and application. The collection can be automated by various contemporary methods for retrieval. The concept should be differentiated from DATABASES, BIBLIOGRAPHIC which is restricted to collections of bibliographic references.

**Quality Control**: A system for verifying and maintaining a desired level of quality in a product or process by careful planning, use of proper equipment, continued inspection, and corrective action as required. (Random House Unabridged Dictionary, 2d ed)

**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.

**Models, Genetic**: Theoretical representations that simulate the behavior or activity of genetic processes or phenomena. They include the use of mathematical equations, computers, and other electronic equipment.

**Data Mining**: Use of sophisticated analysis tools to sort through, organize, examine, and combine large sets of information.

**Diagnosis, Computer-Assisted**: Application of computer programs designed to assist the physician in solving a diagnostic problem.

**Predictive Value of Tests**: In screening and diagnostic tests, the probability that a person with a positive test is a true positive (i.e., has the disease), is referred to as the predictive value of a positive test; whereas, the predictive value of a negative test is the probability that the person with a negative test does not have the disease. Predictive value is related to the sensitivity and specificity of the test.

**Computers, Molecular**: Computers whose input, output and state transitions are carried out by biochemical interactions and reactions.

**Databases, Genetic**: Databases devoted to knowledge about specific genes and gene products.

**Models, Neurological**: Theoretical representations that simulate the behavior or activity of the neurological system, processes or phenomena; includes the use of mathematical equations, computers, and other electronic equipment.

**ROC Curve**: A graphic means for assessing the ability of a screening test to discriminate between healthy and diseased persons; may also be used in other studies, e.g., distinguishing stimuli responses as to a faint stimuli or nonstimuli.

**Sequence Analysis, Protein**: A process that includes the determination of AMINO ACID SEQUENCE of a protein (or peptide, oligopeptide or peptide fragment) and the information analysis of the sequence.

**Confounding Factors (Epidemiology)**: Factors that can cause or prevent the outcome of interest, are not intermediate variables, and are not associated with the factor(s) under investigation. They give rise to situations in which the effects of two processes are not separated, or the contribution of causal factors cannot be separated, or the measure of the effect of exposure or risk is distorted because of its association with other factors influencing the outcome of the study.

**Research Design**: A plan for collecting and utilizing data so that desired information can be obtained with sufficient precision or so that an hypothesis can be tested properly.

**Proteins**: Linear POLYPEPTIDES that are synthesized on RIBOSOMES and may be further modified, crosslinked, cleaved, or assembled into complex proteins with several subunits. The specific sequence of AMINO ACIDS determines the shape the polypeptide will take, during PROTEIN FOLDING, and the function of the protein.

**Statistics as Topic**: The science and art of collecting, summarizing, and analyzing data that are subject to random variation. The term is also applied to the data themselves and to the summarization of the data.

**Odds Ratio**: The ratio of two odds. The exposure-odds ratio for case control data is the ratio of the odds in favor of exposure among cases to the odds in favor of exposure among noncases. The disease-odds ratio for a cohort or cross section is the ratio of the odds in favor of disease among the exposed to the odds in favor of disease among the unexposed. The prevalence-odds ratio refers to an odds ratio derived cross-sectionally from studies of prevalent cases.

**Time Factors**: Elements of limited time intervals, contributing to particular results or situations.

**Likelihood Functions**: Functions constructed from a statistical model and a set of observed data which give the probability of that data for various values of the unknown model parameters. Those parameter values that maximize the probability are the maximum likelihood estimates of the parameters.

**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.

**Monte Carlo Method**: In statistics, a technique for numerically approximating the solution of a mathematical problem by studying the distribution of some random variable, often generated by a computer. The name alludes to the randomness characteristic of the games of chance played at the gambling casinos in Monte Carlo. (From Random House Unabridged Dictionary, 2d ed, 1993)

**Probability**: The study of chance processes or the relative frequency characterizing a chance process.

**Breast Neoplasms**: Tumors or cancer of the human BREAST.

**Risk Assessment**: The qualitative or quantitative estimation of the likelihood of adverse effects that may result from exposure to specified health hazards or from the absence of beneficial influences. (Last, Dictionary of Epidemiology, 1988)

**Prognosis**: A prediction of the probable outcome of a disease based on a individual's condition and the usual course of the disease as seen in similar situations.

**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.

**Image Interpretation, Computer-Assisted**: Methods developed to aid in the interpretation of ultrasound, radiographic images, etc., for diagnosis of disease.

**Computers**

**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.

**Risk Factors**: An aspect of personal behavior or lifestyle, environmental exposure, or inborn or inherited characteristic, which, on the basis of epidemiologic evidence, is known to be associated with a health-related condition considered important to prevent.

**Case-Control Studies**: Studies which start with the identification of persons with a disease of interest and a control (comparison, referent) group without the disease. The relationship of an attribute to the disease is examined by comparing diseased and non-diseased persons with regard to the frequency or levels of the attribute in each group.

**Sample Size**: The number of units (persons, animals, patients, specified circumstances, etc.) in a population to be studied. The sample size should be big enough to have a high likelihood of detecting a true difference between two groups. (From Wassertheil-Smoller, Biostatistics and Epidemiology, 1990, p95)

**Evolution, Molecular**: The process of cumulative change at the level of DNA; RNA; and PROTEINS, over successive generations.

**Photic Stimulation**: Investigative technique commonly used during ELECTROENCEPHALOGRAPHY in which a series of bright light flashes or visual patterns are used to elicit brain activity.

**Poisson Distribution**: A distribution function used to describe the occurrence of rare events or to describe the sampling distribution of isolated counts in a continuum of time or space.

**Image Processing, Computer-Assisted**: A technique of inputting two-dimensional images into a computer and then enhancing or analyzing the imagery into a form that is more useful to the human observer.

**Mathematical Computing**: Computer-assisted interpretation and analysis of various mathematical functions related to a particular problem.

**Markov Chains**: A stochastic process such that the conditional probability distribution for a state at any future instant, given the present state, is unaffected by any additional knowledge of the past history of the system.

**United States**

**Computing Methodologies**: Computer-assisted analysis and processing of problems in a particular area.

**Statistical Distributions**: The complete summaries of the frequencies of the values or categories of a measurement made on a group of items, a population, or other collection of data. The distribution tells either how many or what proportion of the group was found to have each value (or each range of values) out of all the possible values that the quantitative measure can have.

**User-Computer Interface**: The portion of an interactive computer program that issues messages to and receives commands from a user.

**Cluster Analysis**: A set of statistical methods used to group variables or observations into strongly inter-related subgroups. In epidemiology, it may be used to analyze a closely grouped series of events or cases of disease or other health-related phenomenon with well-defined distribution patterns in relation to time or place or both.

**Incidence**: The number of new cases of a given disease during a given period in a specified population. It also is used for the rate at which new events occur in a defined population. It is differentiated from PREVALENCE, which refers to all cases, new or old, in the population at a given time.

**Motion Perception**: The real or apparent movement of objects through the visual field.

**Genetic Variation**: Genotypic differences observed among individuals in a population.

**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).

**Phylogeny**: The relationships of groups of organisms as reflected by their genetic makeup.

**Proportional Hazards Models**: Statistical models used in survival analysis that assert that the effect of the study factors on the hazard rate in the study population is multiplicative and does not change over time.

**Analysis of Variance**: A statistical technique that isolates and assesses the contributions of categorical independent variables to variation in the mean of a continuous dependent variable.

**Mathematics**: The deductive study of shape, quantity, and dependence. (From McGraw-Hill Dictionary of Scientific and Technical Terms, 6th ed)

**Computer Graphics**: The process of pictorial communication, between human and computers, in which the computer input and output have the form of charts, drawings, or other appropriate pictorial representation.

**Logistic Models**: Statistical models which describe the relationship between a qualitative dependent variable (that is, one which can take only certain discrete values, such as the presence or absence of a disease) and an independent variable. A common application is in epidemiology for estimating an individual's risk (probability of a disease) as a function of a given risk factor.

**Linear Models**: Statistical models in which the value of a parameter for a given value of a factor is assumed to be equal to a + bx, where a and b are constants. The models predict a linear regression.

**Nonlinear Dynamics**: The study of systems which respond disproportionately (nonlinearly) to initial conditions or perturbing stimuli. Nonlinear systems may exhibit "chaos" which is classically characterized as sensitive dependence on initial conditions. Chaotic systems, while distinguished from more ordered periodic systems, are not random. When their behavior over time is appropriately displayed (in "phase space"), constraints are evident which are described by "strange attractors". Phase space representations of chaotic systems, or strange attractors, usually reveal fractal (FRACTALS) self-similarity across time scales. Natural, including biological, systems often display nonlinear dynamics and chaos.

**Nerve Net**: A meshlike structure composed of interconnecting nerve cells that are separated at the synaptic junction or joined to one another by cytoplasmic processes. In invertebrates, for example, the nerve net allows nerve impulses to spread over a wide area of the net because synapses can pass information in any direction.

**Action Potentials**: Abrupt changes in the membrane potential that sweep along the CELL MEMBRANE of excitable cells in response to excitation stimuli.

**Genotype**: The genetic constitution of the individual, comprising the ALLELES present at each GENETIC LOCUS.

**Multivariate Analysis**: A set of techniques used when variation in several variables has to be studied simultaneously. In statistics, multivariate analysis is interpreted as any analytic method that allows simultaneous study of two or more dependent variables.

**Treatment Outcome**: Evaluation undertaken to assess the results or consequences of management and procedures used in combating disease in order to determine the efficacy, effectiveness, safety, and practicability of these interventions in individual cases or series.

**Biometry**: The use of statistical and mathematical methods to analyze biological observations and phenomena.

**Internet**: A loose confederation of computer communication networks around the world. The networks that make up the Internet are connected through several backbone networks. The Internet grew out of the US Government ARPAnet project and was designed to facilitate information exchange.

**Neurons**: The basic cellular units of nervous tissue. Each neuron consists of a body, an axon, and dendrites. Their purpose is to receive, conduct, and transmit impulses in the NERVOUS SYSTEM.

**Prospective Studies**: Observation of a population for a sufficient number of persons over a sufficient number of years to generate incidence or mortality rates subsequent to the selection of the study group.

**Genomics**: The systematic study of the complete DNA sequences (GENOME) of organisms.

**Bias (Epidemiology)**: Any deviation of results or inferences from the truth, or processes leading to such deviation. Bias can result from several sources: one-sided or systematic variations in measurement from the true value (systematic error); flaws in study design; deviation of inferences, interpretations, or analyses based on flawed data or data collection; etc. There is no sense of prejudice or subjectivity implied in the assessment of bias under these conditions.

**Stochastic Processes**: Processes that incorporate some element of randomness, used particularly to refer to a time series of random variables.

**Magnetic Resonance Imaging**: Non-invasive method of demonstrating internal anatomy based on the principle that atomic nuclei in a strong magnetic field absorb pulses of radiofrequency energy and emit them as radiowaves which can be reconstructed into computerized images. The concept includes proton spin tomographic techniques.

**Signal Processing, Computer-Assisted**: Computer-assisted processing of electric, ultrasonic, or electronic signals to interpret function and activity.

**Sequence Alignment**: The arrangement of two or more amino acid or base sequences from an organism or organisms in such a way as to align areas of the sequences sharing common properties. The degree of relatedness or homology between the sequences is predicted computationally or statistically based on weights assigned to the elements aligned between the sequences. This in turn can serve as a potential indicator of the genetic relatedness between the organisms.

**Estrogen Receptor Modulators**: Substances that possess antiestrogenic actions but can also produce estrogenic effects as well. They act as complete or partial agonist or as antagonist. They can be either steroidal or nonsteroidal in structure.

**Models, Molecular**: Models used experimentally or theoretically to study molecular shape, electronic properties, or interactions; includes analogous molecules, computer-generated graphics, and mechanical structures.

**Numerical Analysis, Computer-Assisted**: Computer-assisted study of methods for obtaining useful quantitative solutions to problems that have been expressed mathematically.

**Gene Expression Regulation, Neoplastic**: Any of the processes by which nuclear, cytoplasmic, or intercellular factors influence the differential control of gene action in neoplastic tissue.

**Brain**: The part of CENTRAL NERVOUS SYSTEM that is contained within the skull (CRANIUM). Arising from the NEURAL TUBE, the embryonic brain is comprised of three major parts including PROSENCEPHALON (the forebrain); MESENCEPHALON (the midbrain); and RHOMBENCEPHALON (the hindbrain). The developed brain consists of CEREBRUM; CEREBELLUM; and other structures in the BRAIN STEM.

**Polymorphism, Single Nucleotide**: A single nucleotide variation in a genetic sequence that occurs at appreciable frequency in the population.

**Mutation**: Any detectable and heritable change in the genetic material that causes a change in the GENOTYPE and which is transmitted to daughter cells and to succeeding generations.

**Risk**: The probability that an event will occur. It encompasses a variety of measures of the probability of a generally unfavorable outcome.

**Depth Perception**: Perception of three-dimensionality.

**Tumor Markers, Biological**: Molecular products metabolized and secreted by neoplastic tissue and characterized biochemically in cells or body fluids. They are indicators of tumor stage and grade as well as useful for monitoring responses to treatment and predicting recurrence. Many chemical groups are represented including hormones, antigens, amino and nucleic acids, enzymes, polyamines, and specific cell membrane proteins and lipids.

**Neoplasms**: New abnormal growth of tissue. Malignant neoplasms show a greater degree of anaplasia and have the properties of invasion and metastasis, compared to benign neoplasms.

**Quantum Theory**: The theory that the radiation and absorption of energy take place in definite quantities called quanta (E) which vary in size and are defined by the equation E=hv in which h is Planck's constant and v is the frequency of the radiation.

**Cybernetics**: That branch of learning which brings together theories and studies on communication and control in living organisms and machines.

**Selection, Genetic**: Differential and non-random reproduction of different genotypes, operating to alter the gene frequencies within a population.

**Visual Cortex**: Area of the OCCIPITAL LOBE concerned with the processing of visual information relayed via VISUAL PATHWAYS.

**Linkage Disequilibrium**: Nonrandom association of linked genes. This is the tendency of the alleles of two separate but already linked loci to be found together more frequently than would be expected by chance alone.

**Brain Mapping**: Imaging techniques used to colocalize sites of brain functions or physiological activity with brain structures.

**Randomized Controlled Trials as Topic**: Works about clinical trials that involve at least one test treatment and one control treatment, concurrent enrollment and follow-up of the test- and control-treated groups, and in which the treatments to be administered are selected by a random process, such as the use of a random-numbers table.

**Image Enhancement**: Improvement of the quality of a picture by various techniques, including computer processing, digital filtering, echocardiographic techniques, light and ultrastructural MICROSCOPY, fluorescence spectrometry and microscopy, scintigraphy, and in vitro image processing at the molecular level.

**Psychophysics**: The science dealing with the correlation of the physical characteristics of a stimulus, e.g., frequency or intensity, with the response to the stimulus, in order to assess the psychologic factors involved in the relationship.

**Models, Chemical**: Theoretical representations that simulate the behavior or activity of chemical processes or phenomena; includes the use of mathematical equations, computers, and other electronic equipment.

**Visual Perception**: The selecting and organizing of visual stimuli based on the individual's past experience.

**False Positive Reactions**: Positive test results in subjects who do not possess the attribute for which the test is conducted. The labeling of healthy persons as diseased when screening in the detection of disease. (Last, A Dictionary of Epidemiology, 2d ed)

**Chromosome Mapping**: Any method used for determining the location of and relative distances between genes on a chromosome.

**Transplantation, Heterologous**: Transplantation between animals of different species.

**Regression Analysis**: Procedures for finding the mathematical function which best describes the relationship between a dependent variable and one or more independent variables. In linear regression (see LINEAR MODELS) the relationship is constrained to be a straight line and LEAST-SQUARES ANALYSIS is used to determine the best fit. In logistic regression (see LOGISTIC MODELS) the dependent variable is qualitative rather than continuously variable and LIKELIHOOD FUNCTIONS are used to find the best relationship. In multiple regression, the dependent variable is considered to depend on more than a single independent variable.

**Imaging, Three-Dimensional**: The process of generating three-dimensional images by electronic, photographic, or other methods. For example, three-dimensional images can be generated by assembling multiple tomographic images with the aid of a computer, while photographic 3-D images (HOLOGRAPHY) can be made by exposing film to the interference pattern created when two laser light sources shine on an object.

**Visual Pathways**: Set of cell bodies and nerve fibers conducting impulses from the eyes to the cerebral cortex. It includes the RETINA; OPTIC NERVE; optic tract; and geniculocalcarine tract.

**Questionnaires**: Predetermined sets of questions used to collect data - clinical data, social status, occupational group, etc. The term is often applied to a self-completed survey instrument.

**Prostatic Neoplasms**: Tumors or cancer of the PROSTATE.

**Kaplan-Meier Estimate**: A nonparametric method of compiling LIFE TABLES or survival tables. It combines calculated probabilities of survival and estimates to allow for observations occurring beyond a measurement threshold, which are assumed to occur randomly. Time intervals are defined as ending each time an event occurs and are therefore unequal. (From Last, A Dictionary of Epidemiology, 1995)

**Neoplasm Staging**: Methods which attempt to express in replicable terms the extent of the neoplasm in the patient.

**Thermodynamics**: A rigorously mathematical analysis of energy relationships (heat, work, temperature, and equilibrium). It describes systems whose states are determined by thermal parameters, such as temperature, in addition to mechanical and electromagnetic parameters. (From Hawley's Condensed Chemical Dictionary, 12th ed)

**Molecular Sequence Data**: Descriptions of specific amino acid, carbohydrate, or nucleotide sequences which have appeared in the published literature and/or are deposited in and maintained by databanks such as GENBANK, European Molecular Biology Laboratory (EMBL), National Biomedical Research Foundation (NBRF), or other sequence repositories.

**Lung Neoplasms**: Tumors or cancer of the LUNG.

**Mathematical Concepts**: Numeric or quantitative entities, descriptions, properties, relationships, operations, and events.

**Chemotherapy, Adjuvant**: Drug therapy given to augment or stimulate some other form of treatment such as surgery or radiation therapy. Adjuvant chemotherapy is commonly used in the therapy of cancer and can be administered before or after the primary treatment.

**Anticarcinogenic Agents**: Agents that reduce the frequency or rate of spontaneous or induced tumors independently of the mechanism involved.

**SEER Program**: A cancer registry mandated under the National Cancer Act of 1971 to operate and maintain a population-based cancer reporting system, reporting periodically estimates of cancer incidence and mortality in the United States. The Surveillance, Epidemiology, and End Results (SEER) Program is a continuing project of the National Cancer Institute of the National Institutes of Health. Among its goals, in addition to assembling and reporting cancer statistics, are the monitoring of annual cancer incident trends and the promoting of studies designed to identify factors amenable to cancer control interventions. (From National Cancer Institute, NIH Publication No. 91-3074, October 1990)

**Genetic Markers**: A phenotypically recognizable genetic trait which can be used to identify a genetic locus, a linkage group, or a recombination event.

**Statistics, Nonparametric**: A class of statistical methods applicable to a large set of probability distributions used to test for correlation, location, independence, etc. In most nonparametric statistical tests, the original scores or observations are replaced by another variable containing less information. An important class of nonparametric tests employs the ordinal properties of the data. Another class of tests uses information about whether an observation is above or below some fixed value such as the median, and a third class is based on the frequency of the occurrence of runs in the data. (From McGraw-Hill Dictionary of Scientific and Technical Terms, 4th ed, p1284; Corsini, Concise Encyclopedia of Psychology, 1987, p764-5)

**Survival Analysis**: A class of statistical procedures for estimating the survival function (function of time, starting with a population 100% well at a given time and providing the percentage of the population still well at later times). The survival analysis is then used for making inferences about the effects of treatments, prognostic factors, exposures, and other covariates on the function.

**Immunohistochemistry**: Histochemical localization of immunoreactive substances using labeled antibodies as reagents.

**Biological Evolution**: The process of cumulative change over successive generations through which organisms acquire their distinguishing morphological and physiological characteristics.

**Haplotypes**: The genetic constitution of individuals with respect to one member of a pair of allelic genes, or sets of genes that are closely linked and tend to be inherited together such as those of the MAJOR HISTOCOMPATIBILITY COMPLEX.

**Polymorphism, Genetic**: The regular and simultaneous occurrence in a single interbreeding population of two or more discontinuous genotypes. The concept includes differences in genotypes ranging in size from a single nucleotide site (POLYMORPHISM, SINGLE NUCLEOTIDE) to large nucleotide sequences visible at a chromosomal level.

**Gene Frequency**: The proportion of one particular in the total of all ALLELES for one genetic locus in a breeding POPULATION.

**Follow-Up Studies**: Studies in which individuals or populations are followed to assess the outcome of exposures, procedures, or effects of a characteristic, e.g., occurrence of disease.

**Genetic Predisposition to Disease**: A latent susceptibility to disease at the genetic level, which may be activated under certain conditions.

**Retrospective Studies**: Studies used to test etiologic hypotheses in which inferences about an exposure to putative causal factors are derived from data relating to characteristics of persons under study or to events or experiences in their past. The essential feature is that some of the persons under study have the disease or outcome of interest and their characteristics are compared with those of unaffected persons.

**Base Sequence**: The sequence of PURINES and PYRIMIDINES in nucleic acids and polynucleotides. It is also called nucleotide sequence.

**Antineoplastic Agents, Hormonal**: Antineoplastic agents that are used to treat hormone-sensitive tumors. Hormone-sensitive tumors may be hormone-dependent, hormone-responsive, or both. A hormone-dependent tumor regresses on removal of the hormonal stimulus, by surgery or pharmacological block. Hormone-responsive tumors may regress when pharmacologic amounts of hormones are administered regardless of whether previous signs of hormone sensitivity were observed. The major hormone-responsive cancers include carcinomas of the breast, prostate, and endometrium; lymphomas; and certain leukemias. (From AMA Drug Evaluations Annual 1994, p2079)

**Antineoplastic Agents**: Substances that inhibit or prevent the proliferation of NEOPLASMS.

**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.

**Cohort Studies**: Studies in which subsets of a defined population are identified. These groups may or may not be exposed to factors hypothesized to influence the probability of the occurrence of a particular disease or other outcome. Cohorts are defined populations which, as a whole, are followed in an attempt to determine distinguishing subgroup characteristics.

**Logic**: The science that investigates the principles governing correct or reliable inference and deals with the canons and criteria of validity in thought and demonstration. This system of reasoning is applicable to any branch of knowledge or study. (Random House Unabridged Dictionary, 2d ed & Sippl, Computer Dictionary, 4th ed)

**Genome**: The genetic complement of an organism, including all of its GENES, as represented in its DNA, or in some cases, its RNA.

**Age Factors**: Age as a constituent element or influence contributing to the production of a result. It may be applicable to the cause or the effect of a circumstance. It is used with human or animal concepts but should be differentiated from AGING, a physiological process, and TIME FACTORS which refers only to the passage of time.

**Normal Distribution**: Continuous frequency distribution of infinite range. Its properties are as follows: 1, continuous, symmetrical distribution with both tails extending to infinity; 2, arithmetic mean, mode, and median identical; and 3, shape completely determined by the mean and standard deviation.

**Mammography**: Radiographic examination of the breast.

**Genome-Wide Association Study**: An analysis comparing the allele frequencies of all available (or a whole GENOME representative set of) polymorphic markers in unrelated patients with a specific symptom or disease condition, and those of healthy controls to identify markers associated with a specific disease or condition.

**Vision Disparity**: The difference between two images on the retina when looking at a visual stimulus. This occurs since the two retinas do not have the same view of the stimulus because of the location of our eyes. Thus the left eye does not get exactly the same view as the right eye.

**Reaction Time**: The time from the onset of a stimulus until a response is observed.

**DNA**: A deoxyribonucleotide polymer that is the primary genetic material of all cells. Eukaryotic and prokaryotic organisms normally contain DNA in a double-stranded state, yet several important biological processes transiently involve single-stranded regions. DNA, which consists of a polysugar-phosphate backbone possessing projections of purines (adenine and guanine) and pyrimidines (thymine and cytosine), forms a double helix that is held together by hydrogen bonds between these purines and pyrimidines (adenine to thymine and guanine to cytosine).

**Genetic Linkage**: The co-inheritance of two or more non-allelic GENES due to their being located more or less closely on the same CHROMOSOME.

**Radiographic Image Interpretation, Computer-Assisted**: Computer systems or networks designed to provide radiographic interpretive information.

**Principal Component Analysis**: Mathematical procedure that transforms a number of possibly correlated variables into a smaller number of uncorrelated variables called principal components.

**Motion**: Physical motion, i.e., a change in position of a body or subject as a result of an external force. It is distinguished from MOVEMENT, a process resulting from biological activity.

**Periodicals as Topic**: A publication issued at stated, more or less regular, intervals.

**Phenotype**: The outward appearance of the individual. It is the product of interactions between genes, and between the GENOTYPE and the environment.

**Programming Languages**: Specific languages used to prepare computer programs.

**Alleles**: Variant forms of the same gene, occupying the same locus on homologous CHROMOSOMES, and governing the variants in production of the same gene product.

**Anesthesiology**: A specialty concerned with the study of anesthetics and anesthesia.

**Disease-Free Survival**: Period after successful treatment in which there is no appearance of the symptoms or effects of the disease.

**Models, Psychological**: Theoretical representations that simulate psychological processes and/or social processes. These include the use of mathematical equations, computers, and other electronic equipment.

**Space Perception**: The awareness of the spatial properties of objects; includes physical space.

**Pattern Recognition, Visual**: Mental process to visually perceive a critical number of facts (the pattern), such as characters, shapes, displays, or designs.

**Orientation**: Awareness of oneself in relation to time, place and person.

**Learning**: Relatively permanent change in behavior that is the result of past experience or practice. The concept includes the acquisition of knowledge.

**Biostatistics**: The application of STATISTICS to biological systems and organisms involving the retrieval or collection, analysis, reduction, and interpretation of qualitative and quantitative data.

**Matched-Pair Analysis**: A type of analysis in which subjects in a study group and a comparison group are made comparable with respect to extraneous factors by individually pairing study subjects with the comparison group subjects (e.g., age-matched controls).

**Artifacts**: Any visible result of a procedure which is caused by the procedure itself and not by the entity being analyzed. Common examples include histological structures introduced by tissue processing, radiographic images of structures that are not naturally present in living tissue, and products of chemical reactions that occur during analysis.

**Epistasis, Genetic**: A form of gene interaction whereby the expression of one gene interferes with or masks the expression of a different gene or genes. Genes whose expression interferes with or masks the effects of other genes are said to be epistatic to the effected genes. Genes whose expression is affected (blocked or masked) are hypostatic to the interfering genes.

**Molecular Conformation**: The characteristic three-dimensional shape of a molecule.

**Selection Bias**: The introduction of error due to systematic differences in the characteristics between those selected and those not selected for a given study. In sampling bias, error is the result of failure to ensure that all members of the reference population have a known chance of selection in the sample.

**Psychomotor Performance**: The coordination of a sensory or ideational (cognitive) process and a motor activity.

**Movement**: The act, process, or result of passing from one place or position to another. It differs from LOCOMOTION in that locomotion is restricted to the passing of the whole body from one place to another, while movement encompasses both locomotion but also a change of the position of the whole body or any of its parts. Movement may be used with reference to humans, vertebrate and invertebrate animals, and microorganisms. Differentiate also from MOTOR ACTIVITY, movement associated with behavior.

**Bionics**: The study of systems, particularly electronic systems, which function after the manner of, in a manner characteristic of, or resembling living systems. Also, the science of applying biological techniques and principles to the design of electronic systems.

**Information Theory**: An interdisciplinary study dealing with the transmission of messages or signals, or the communication of information. Information theory does not directly deal with meaning or content, but with physical representations that have meaning or content. It overlaps considerably with communication theory and CYBERNETICS.

**European Continental Ancestry Group**: Individuals whose ancestral origins are in the continent of Europe.

**Meta-Analysis as Topic**: A quantitative method of combining the results of independent studies (usually drawn from the published literature) and synthesizing summaries and conclusions which may be used to evaluate therapeutic effectiveness, plan new studies, etc., with application chiefly in the areas of research and medicine.

**Systems Biology**: Comprehensive, methodical analysis of complex biological systems by monitoring responses to perturbations of biological processes. Large scale, computerized collection and analysis of the data are used to develop and test models of biological systems.

**Pedigree**: The record of descent or ancestry, particularly of a particular condition or trait, indicating individual family members, their relationships, and their status with respect to the trait or condition.

**Colorectal Neoplasms**: Tumors or cancer of the COLON or the RECTUM or both. Risk factors for colorectal cancer include chronic ULCERATIVE COLITIS; FAMILIAL POLYPOSIS COLI; exposure to ASBESTOS; and irradiation of the CERVIX UTERI.

**Scandinavia**

**Drug Administration Schedule**: Time schedule for administration of a drug in order to achieve optimum effectiveness and convenience.

**Physical Phenomena**: The entities of matter and energy, and the processes, principles, properties, and relationships describing their nature and interactions.

**Macaca mulatta**: A species of the genus MACACA inhabiting India, China, and other parts of Asia. The species is used extensively in biomedical research and adapts very well to living with humans.

**Eye Movements**: Voluntary or reflex-controlled movements of the eye.

**Registries**: The systems and processes involved in the establishment, support, management, and operation of registers, e.g., disease registers.

**Europe**

**Cues**: Signals for an action; that specific portion of a perceptual field or pattern of stimuli to which a subject has learned to respond.

**Form Perception**: The sensory discrimination of a pattern shape or outline.

**Radiotherapy, Adjuvant**: Radiotherapy given to augment some other form of treatment such as surgery or chemotherapy. Adjuvant radiotherapy is commonly used in the therapy of cancer and can be administered before or after the primary treatment.

**Biophysics**: The study of PHYSICAL PHENOMENA and PHYSICAL PROCESSES as applied to living things.

**Dendrites**: Extensions of the nerve cell body. They are short and branched and receive stimuli from other NEURONS.

**Microsatellite Repeats**: A variety of simple repeat sequences that are distributed throughout the GENOME. They are characterized by a short repeat unit of 2-8 basepairs that is repeated up to 100 times. They are also known as short tandem repeats (STRs).

**Tamoxifen**: One of the SELECTIVE ESTROGEN RECEPTOR MODULATORS with tissue-specific activities. Tamoxifen acts as an anti-estrogen (inhibiting agent) in the mammary tissue, but as an estrogen (stimulating agent) in cholesterol metabolism, bone density, and cell proliferation in the ENDOMETRIUM.

**Smoking**: Inhaling and exhaling the smoke of burning TOBACCO.

**Entropy**: The measure of that part of the heat or energy of a system which is not available to perform work. Entropy increases in all natural (spontaneous and irreversible) processes. (From Dorland, 28th ed)

**Mental Processes**: Conceptual functions or thinking in all its forms.

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**models used for causal inference in epidemiology. Such models handle the issue of time-dependent confounding in evaluation of the efficacy of interventions by inverse probability weighting for receipt of treatment. For instance, in the study of the effect of zidovudine in AIDS-related mortality, CD4 lymphocyte is used both for treatment indication, is influenced by treatment, and affects survival. Time-dependent confounders are typically highly prognostic of health outcomes and applied in dosing or indication for certain therapies, such as body weight or lab values such as alanine aminotransferase or bilirubin. Robins, James; Hernán, Miguel; Brumback, Babette (September 2000). "Marginal Structural Models and Causal Inference in Epidemiology" (PDF). Epidemiology. 11 (5): 550-60. doi:10.1097/00001648-200009000-00011. PMID 10955408. https://epiresearch.org/ser50/serplaylists/introduction-to-marginal-structural-models ...****statistical**Significance testing is largely the product of Karl Pearson (p-value, Pearson's chi-squared test), William Sealy Gosset (Student's t-distribution), and Ronald Fisher ("null hypothesis", analysis of variance, "significance test"), while hypothesis testing was developed by Jerzy Neyman and Egon Pearson (son of Karl). Ronald Fisher began his life in statistics as a Bayesian (Zabell 1992), but Fisher soon grew disenchanted with the subjectivity involved (namely use of the principle of indifference when determining prior probabilities), and sought to provide a more "objective" approach to inductive inference.[22]. Fisher was an agricultural statistician who emphasized rigorous experimental design and methods to extract a result from few samples assuming Gaussian distributions. Neyman (who teamed with the younger Pearson) emphasized mathematical rigor and methods to obtain more results from many samples and a wider range of distributions. Modern hypothesis testing is an inconsistent hybrid of the ...

In statistics, the bias (or bias function) of an estimator is the difference between this estimator's expected value and the true value of the parameter being estimated. An estimator or decision rule with zero bias is called unbiased. Otherwise the estimator is said to be biased. In statistics, "bias" is an objective property of an estimator, and while not a desired property, it is not pejorative, unlike the ordinary English use of the term "bias". Bias can also be measured with respect to the median, rather than the mean (expected value), in which case one distinguishes median-unbiased from the usual mean-unbiasedness property. Bias is related to consistency in that consistent estimators are convergent and asymptotically unbiased (hence converge to the correct value as the number of

**data**points grows arbitrarily large), though individual estimators in a consistent sequence may be biased (so long as the bias converges to zero); see bias versus consistency. All else being equal, an unbiased ...In statistics, sampling error is the error caused by observing a sample instead of the whole population.[1] The sampling error is the difference between a sample statistic used to estimate a population parameter and the actual but unknown value of the parameter.[2] An estimate of a quantity of interest, such as an average or percentage, will generally be subject to sample-to-sample variation.[1] These variations in the possible sample values of a statistic can theoretically be expressed as sampling errors, although in practice the exact sampling error is typically unknown. Sampling error also refers more broadly to this phenomenon of random sampling variation. Random sampling, and its derived terms such as sampling error, imply specific procedures for gathering and analyzing

**data**that are rigorously applied as a method for arriving at results considered representative of a given population as a whole. Despite a common misunderstanding, "random" does not mean the same thing as "chance" as ...Arpad Elo was a master-level chess player and an active participant in the United States Chess Federation (USCF) from its founding in 1939.[3] The USCF used a numerical ratings system, devised by Kenneth Harkness, to allow members to track their individual progress in terms other than tournament wins and losses. The Harkness system was reasonably fair, but in some circumstances gave rise to ratings which many observers considered inaccurate. On behalf of the USCF, Elo devised a new system with a more sound

**basis. Elo's system replaced earlier systems of competitive rewards with a system based on****statistical****estimation. Rating systems for many sports award points in accordance with subjective evaluations of the 'greatness' of certain achievements. For example, winning an important golf tournament might be worth an arbitrarily chosen five times as many points as winning a lesser tournament. A ...****statistical**These can be arranged into a 2×2 contingency table, with columns corresponding to actual value - condition positive (CP) or condition negative (CN) - and rows corresponding to classification value - test outcome positive (OP) or test outcome negative (ON). There are eight basic ratios that one can compute from this table, which come in four complementary pairs (each pair summing to 1). These are obtained by dividing each of the four numbers by the sum of its row or column, yielding eight numbers, which can be referred to generically in the form "true positive row ratio" or "false negative column ratio", though there are conventional terms. There are thus two pairs of column ratios and two pairs of row ratios, and one can summarize these with four numbers by choosing one ratio from each pair - the other four numbers are the complements. The column ratios are True Positive Rate (TPR, aka Sensitivity or recall) (TP/(TP+FN)), with complement the False Negative Rate (FNR) (FN/(TP+FN)); and True ...

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the likelihood ratio is therefore a statistic. The likelihood ratio test rejects the null hypothesis if the value of this statistic is too small. How small is too small depends on the significance level of the test, i.e., on what probability of Type I error is considered tolerable ("Type I" errors consist of the rejection of a null hypothesis that is true).. The numerator corresponds to the likelihood of an observed outcome under the null hypothesis. The denominator corresponds to the maximum likelihood of an observed outcome varying parameters over the whole parameter space. The numerator of this ratio is less than the denominator. The likelihood ratio hence is between 0 and 1. Low values of the likelihood ratio mean that the observed result was less likely to occur under the null hypothesis as compared to the alternative. High values of the statistic mean that the observed outcome was nearly as likely to occur under the null hypothesis as the alternative, and the null hypothesis cannot be ...

where µ is the mean, ν is the median, and σ is the standard deviation, the skewness is defined in terms of this relationship: positive/right non

**parametric**skew means the mean is greater than (to the right of) the median, while negative/left non**parametric**skew means the mean is less than (to the left of) the median. However, the modern definition of skewness and the traditional non**parametric**definition do not in general have the same sign: while they agree for some families of distributions, they differ in general, and conflating them is misleading.. If the distribution is symmetric, then the mean is equal to the median, and the distribution has zero skewness.[2] If, in addition, the distribution is unimodal, then the mean = median = mode. This is the case of a coin toss or the series 1,2,3,4,... Note, however, that the converse is not true in general, i.e. zero skewness does not imply that the mean is equal to the median.. Paul T. von Hippel points out: ...... has applications in

**inference. For example, one might use it to fit an isotonic curve to the means of some set of experimental results when an increase in those means according to some particular ordering is expected. A benefit of isotonic regression is that it is not constrained by any functional form, such as the linearity imposed by linear regression, as long as the function is monotonic increasing. Another application is nonmetric multidimensional scaling,[1] where a low-dimensional embedding for****statistical****data**points is sought such that order of distances between points in the embedding matches order of dissimilarity between points. Isotonic regression is used iteratively to fit ideal distances to preserve relative dissimilarity order. Software for computing isotone (monotonic) regression has been developed for the R**package [2], the Stata****statistical****package and the ...****statistical**In a regression model setting, the goal is to establish whether or not a relationship exists between a response variable and a set of predictor variables. Further, if a relationship does exist, the goal is then to be able to describe this relationship as best as possible. A main assumption in linear regression is constant variance or (homoscedasticity), meaning that different response variables have the same variance in their errors, at every predictor level. This assumption works well when the response variable and the predictor variable are jointly Normal, see Normal distribution. As we will see later, the variance function in the Normal setting, is constant, however, we must find a way to quantify heteroscedasticity (non-constant variance) in the absence of joint Normality. When it is likely that the response follows a distribution that is a member of the exponential family, a generalized linear model may be more appropriate to use, and moreover, when we wish not to force a

**parametric**...Looking for patterns in

**data**is legitimate. Applying a**test of significance, or hypothesis test, to the same****statistical****data**that a pattern emerges from is wrong. One way to construct hypotheses while avoiding**data**dredging is to conduct randomized out-of-sample**tests**. The researcher collects a**data**set, then randomly partitions it into two subsets, A and B. Only one subset-say, subset A-is examined for creating hypotheses. Once a hypothesis is formulated, it must be tested on subset B, which was not used to construct the hypothesis. Only where B also supports such a hypothesis is it reasonable to believe the hypothesis might be valid. (This is a simple type of cross-validation and is often termed training-test or split-half validation.) Another remedy for**data**dredging is to record the number of all significance**tests**conducted during the study and simply ...চিকাগো বিশ্বোবিদ্যালয়ের statistics শিক্ষক স্টিফেন স্টিগলার, ১৯৮০ সালে প্রোকাশিতো বোই stigler's law of eponyms-এ উপোরোক্ত বিষয়ে বিস্তারিতো বর্ণোনা দ্যান।[১] তিনি রবার্ট মার্টন, হুবার্ট কেনেডি, মার্ক টোয়েন, কার্ল বোয়ের, বাবা জর্জ স্টিগলার etc.দের দ্বারা ওনুপ্রানিতো হয়ে এটি লেখেন। সবার অ্যাকই বক্তব্যো, কাজ করে এ, নাম হয় ওর। [২] মার্ক তোয়েন বোলেছেন, টেলিগ্রাফ/টেলিফোন/বাষ্পো ...

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**Interpretation**of Online Ratings**Data**with Model-Based Figures. by Ho, Daniel E & Quinn ... 147-154 Easy Multiplicity Control in Equivalence**Testing**Using Two One-Sided**Tests**. by Lauzon, Carolyn & Caffo, Brian *155-162 ... 78-80 The Mean, Median, and Confidence Intervals of the Kaplan-Meier Survival Estimateâ€"**Computations**and Applications. by ... 296-306**Parametric**Nonparametric Statistics. by Christensen, Ronald & Hanson, Timothy & Jara, Alejandro *307-313 Flexible ...**Statistical**analysis of

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**test**is related to a number of other non-**parametric****statistical**procedures. For example, it is equivalent to ... Ordinal**data**The Mann-Whitney U**test**remains the logical choice when the**data**are ordinal but not interval scaled, so that the ... If one desires a simple shift**interpretation**, the Mann-Whitney U**test**should not be used when the distributions of the two ... A thorough analysis of the**statistic**, which included a recurrence allowing the**computation**of tail probabilities for arbitrary ...Introduces

**parametric**inferencesusing the exponential and Weibull distributions. ... Focuses on using**data**sets from clinical and epidemiological studies to illustrate the introduced**statistical**methods and to ... show how to make scientific**interpretations**from the numerical results. SAS and Stata are the**computation**softwares used in ... Kaplan-Meier curves and logrank**tests**. Introduces**parametric**inferencesusing the exponential and Weibull distributions. Also ...This allows the

**data**to be used in powerful**statistical**packages to apply**data**mining techniques, such as supervised ( ... In particular, a software that could facilitate the recording and the**interpretation**of**data**gathered in natural contexts from ... In this case, we applied non-**parametric**statistics with**statistical**software packages. ... at which point more sophisticated**data**calculation and**computation**abilities are required. ...... this volume explores the

**statistical**methods of examining time intervals between successive state transitions or events. ... The**Statistical**Theory of Event History Analysis.**Data**Organization and Descriptive Methods. Semi-**Parametric**Regression Models ... who are bound to find this text very helpful as a work of reference when they set up their**computations**." ... demonstrates, through examples, how to implement hypotheses**tests**and how to choose the right model. ...... s

**test**for categorical**data**. Selected non-**parametric**techniques are included. ... The**computation**and**interpretation**of confidence intervals are illustrated with examples that will catch the reader's ... is used to provide numerical evidence supporting the conclusions of clinical studies and how to evaluate the use of**statistical**... Topics include summarization of**data**, comparison of groups (the one-way analysis of variance and the two-sample t-**test**), and ...This course covers the

**statistical**measurement and analysis methods relevant to the study of pharmacokinetics, dose-response ... Analysis of illustrative**data**using two sample**tests*****Test**for carry over effect ...**Computations**involved would require use of some**statistical**software. Participants can use any software convenient to them. ...**Parametric**(AUC, Cmax) and Non-**parametric****tests**(Tmax). *Bootstrap confidence interval for t1/2 ...Use of

**statistical**software to manage, process and analyze**data**. Writing of**statistical**programs to perform simulation ... nonparametric**tests**, goodness-of-fit**tests**and ANOVA. In order to fully comprehend the**statistical**analysis of those ... Applications and**interpretation**of numerical information in context. Selection and use of appropriate tools: scientific ... Emphasis on**computations**and applications to fluid and heat flow. Prerequisite: MATH 237. ..."A Distribution-Free

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**statistical**surface-based morphometry based on the use of non-**parametric**permutation**tests**and a ... Magnetic resonance volume**data**has much lower resolution than histological image**data**, but it includes the entire liver volume ... Direct**interpretation**of the dynamics and the functionality of these structures with physical models, is yet to be developed. ... The goal of this project is for better visualizing and**computation**of neural activity from fMRI brain imagery. Also, with this ...Specific topics include applications of

**statistical**techniques such as point and interval estimation, hypothesis**testing**(**tests**... This course represents an introduction to the field and provides a survey of**data**types and analysis techniques. ... While the course emphasizes**interpretation**and concepts, there are also formulae and computational elements such that upon ... Provides an introduction to selected important topics in**statistical**concepts and reasoning. ...Random forests (RF) is a powerful classification tree approach to finding patterns in

**data**, but, as with classical**parametric**... The conceptual challenge springs from two different fundamental**interpretations**of the term, one functional and the other ... This question is for**testing**whether you are a human visitor and to prevent automated spam submissions. ...**Statistical**geneticists initially worked mainly with linear models and other**parametric**methods. When applied to genetic ...Role of funding source: The funding source had no role in the design of this study, the analyses and

**interpretation**of the**data**... Statistics. The**parametric**Pearsons**test**was used to calculate correlations between AFD and lung function. Univariate and ... All analyses were performed using**Statistical**Package for the Social Sciences (SPSS V 24.0, SPSS) and R**statistical**software (V ... All fractal**computations**were performed using MATLAB software (Math Works).. Figure 3. Segmented airway tree and AFD in ...Correspondingly, a large number of

**statistical**approaches for detecting gene set enrichment have been proposed, but both the ... a computer simulation comparing 261 different variants of gene set enrichment procedures and to analyze two experimental**data**... We conduct an extensive survey of**statistical**approaches for gene set analysis and identify a common modular structure ... Analysis of microarray and other high-throughput**data**on the basis of gene sets, rather than individual genes, is becoming more ...... is required with an emphasis on

**interpretation**and evaluation of**statistical**results. Topics must include**data**collection ... ESL 073 with required writing placement**test**score; or ESL 074 with required reading placement**test**score. ... The use of technology-based**computations**(more advanced than a basic scientific calculator, such as graphing calculators with a ... polar coordinates and**parametric**equations with applications to science and engineering. IAI M1 900-2, IAI MTH 902 ...... adequate sample size to achieve

**statistical**power, and radiologist blinding during image**interpretation**. The system has a ... These**data**should therefore be taken as proof of the concept that PAI can depict changes to vascular beds in general rather ... This was calculated separately for each reader and compared to chance (50% rate) using a one-sample**test**of proportions. ... Here, we used a fibre-coupled, 30 Hz, optical**parametric**oscillator (OPO) excitation laser system (SpitLight-600, InnoLas Laser ...A Mann-Whitney

**test**, which is a non-**parametric****test**.. A non-**parametric**means that there is no assumption regarding the ... the**test****statistic**such as, which is were making the T**test**where. ... Two major reasoning threads are: the design, execution and**interpretation**of multivariable experiments that produce large**data**... Why do we need**computation**and simulations to understand these systems? The course will develop multiple lines of reasoning to ...Mathematical currents as non-

**parametric**shape descriptors. The current of a surface S is defined as the flux of a**test**vector ... contributed to the**data**analysis and**interpretation**and drafted the manuscript. GB and CC contributed to the**data**acquisition ... All**computations**were performed on a workstation with 32GB memory using 10 cores. Computational time was recorded. Results ... Widely used**parametric**methods to build**statistical**shape models are based on the so called Point Distribution Model (PDM) [5 ...InferenceHypothesisMethodsNumericalProportional hazards modelDistributionsNeuralAnalysesAnalysisEstimationProbabilityExponentialMethodologyHypothesesBayesianBootstrapStataANOVAModelsApproachesAnalyticsVarianceAlgorithmsGraphsCalculationDistributional assumptionsStandard statisticalExamplesAssumptionsBayesParametersAnalyzeGenomicsComputationalMeasurementsAlgorithmPredictiveBiostatisticsAnalyticEstimateClinicalAppendicesStatisticsLikelihoodBiologicalSignificanceHigh-dimensional dataModelInferencesInvolvesSymbolicPriorsAmountsAbstractOptimalIntroduction

- Statistics provides quantitative inference represented as long-time probability values, confidence or prediction intervals, odds, chances, etc., which may ultimately be subjected to varying interpretations. (ucla.edu)
- However, most of the time, common principles of logic allow us to disambiguate the obtained statistical inference. (ucla.edu)
- The module will show you some of the problems with frequentist statistical methods, show you that the Bayesian paradigm provides a unified approach to problems of statistical inference and prediction, enable you to make Bayesian inferences in a variety of problems, and illustrate the use of Bayesian methods in real-life examples. (qmul.ac.uk)
- This module introduces modern methods of statistical inference for small samples, which use computational methods of analysis, rather than asymptotic theory. (qmul.ac.uk)
- When \(y\) represents data and \(\theta\) represents parameters in a statistical model, Bayes Theorem provides the basis for Bayesian inference . (scholarpedia.org)
- Topics covered will include modeling and estimation of data from heavy-tailed distributions, models and inference with multivariate copulas, linear and non-linear time series analysis, and statistical portfolio modeling. (umich.edu)
- download distributed computer control systems 1982 proceedings of the fourth ifac Chart A estimation comparison is a Basic term as it does the natural usage of each inference example by the 360 data of the vertices. (noflair.com)
- In this context, expectations are particularly interesting, because they can be viewed as prior beliefs in the statistical inference process. (frontiersin.org)
- Using, as much as possible, the material from these talks, we give an overview of modern genomics: from the essential assays that make data-generation possible, to the statistical methods that yield meaningful inference. (royalsocietypublishing.org)
- A typical microarray experiment can produce millions of data points, raising serious problems of data reduction, and simultaneous inference. (psu.edu)
- Hasinur Khan works in high-dimensional data analysis (both censored or complete), with his main research interest being in variable selection, model selection with regularized techniques and in statistical inference with censored data found in fields like Statistical Genomics, Bioinformatics, Biostatistics etc. (ac.bd)
- My main project during the last two years has been 'Computer Age Statistical Inference', a book written in collaboration with Trevor Hastie. (stanford.edu)
- For proper statistical inference, the exact sample size required should be calculated and used. (statstodo.com)
- Students are introduced to the data summaries and presentation, statistical inference (including hypothesis testing, p-values, and confidence intervals), sample size calculation, and modeling approaches such as regression analysis. (uw.edu)

- In this course students will engage in each step of the research process including developing a hypothesis, conducting a literature review, designing a study, collecting data, analyzing data, and writing up and presenting the results. (losrios.edu)
- basic statistical measures of central tendency and of dispersion, frequency distributions, elements of probability, binomial and normal distributions, small and large sample hypothesis testing, confidence intervals, chi square test, and regression. (stonybrook.edu)
- In statistics, the Mann-Whitney U test (also called the Mann-Whitney-Wilcoxon (MWW), Wilcoxon rank-sum test, or Wilcoxon-Mann-Whitney test) is a nonparametric test of the null hypothesis that it is equally likely that a randomly selected value from one sample will be less than or greater than a randomly selected value from a second sample. (wikipedia.org)
- Although Mann and Whitney developed the Mann-Whitney U test under the assumption of continuous responses with the alternative hypothesis being that one distribution is stochastically greater than the other, there are many other ways to formulate the null and alternative hypotheses such that the Mann-Whitney U test will give a valid test. (wikipedia.org)
- The test involves the calculation of a statistic, usually called U, whose distribution under the null hypothesis is known. (wikipedia.org)
- Specific topics include applications of statistical techniques such as point and interval estimation, hypothesis testing (tests of significance), correlation and regression, relative risks and odds ratios, sample size/power calculations and study designs. (umc.edu)
- We tested this hypothesis by scanning human subjects using functional MRI while they tasted wines that, contrary to reality, they believed to be different and sold at different prices. (pnas.org)
- We demonstrate that frog measurement data meet assumptions for clearly defined statistical hypothesis testing with statistical linear models rather than those of exploratory multivariate techniques such as principal components, correlation or correspondence analysis. (scielo.br)
- P ( D ∣ H ) measures how compatible the data is with the hypothesis and is called the "likelihood. (frontiersin.org)
- The "prior" P ( H ) corresponds to one's prior expectations about the probability of the hypothesis, and serves to interpret the data in situations of uncertainty. (frontiersin.org)
- The study of expectations, of statistical and perceptual learning, and the so-called "Bayesian Brain hypothesis" have developed somewhat independently. (frontiersin.org)
- Significance tests [ 1 ] are regarded as procedures for measuring the consistency of data with a null hypothesis by the calculation of a p-value (tail area under the null hypothesis). (mdpi.com)
- The most important argument against Bayesian test for precise hypothesis is presented by [ 5 ]. (mdpi.com)
- If the null hypothesis of no association is true, then the calculated test statistic approximately follows a χ 2 distribution with (r - 1) × (c - 1) degrees of freedom (where r is the number of rows and c the number of columns). (biomedcentral.com)
- Precisely, we tested the hypothesis that intertrial variability (ITV) in brain regions coding PPS predicts individual differences of its boundary at the behavioral level. (jneurosci.org)
- In practice, this is the hypothesis that is being tested in an experiment. (tripod.com)
- Uses case studies and examples from popular and scientific literature to introduce topics such as data description, study design, screening, estimation hypothesis testing, categorical data analysis, and regression. (uw.edu)
- Probability, point and confidence interval estimation, hypothesis testing including two-sample and paired t and chi-square tests, introduction to simple linear regression. (uw.edu)

- Current methods of obtaining data (e.g. neuroimaging techniques) will be examined and evaluated. (losrios.edu)
- The application of current statistical methods to problems in the modern business environment. (stonybrook.edu)
- Focuses on using data sets from clinical and epidemiological studies to illustrate the introduced statistical methods and to show how to make scientific interpretations from the numerical results. (jhsph.edu)
- Serving as both a student textbook and a professional reference/handbook, this volume explores the statistical methods of examining time intervals between successive state transitions or events. (routledge.com)
- Data Organization and Descriptive Methods. (routledge.com)
- This online course, "Clinical Trials - Phamacokinetics and Bioequivalence" covers the statistical measurement and analysis methods relevant to the study of pharmacokinetics (the absorption, distribution and secretion of drugs), dose-response modeling and bioequivalence. (statistics.com)
- This includes designing the study in accordance with regulatory requirements, as well as appropriate methods for analyzing data. (statistics.com)
- Statistical Methods in Research. (umc.edu)
- A continuation of Statistical Methods in Research 1, this course introduces the student to more complicated methods than those discussed in the first course including generalized linear models, survival models and longitudinal data analysis. (umc.edu)
- Statistical Methods for Clinical Trials. (umc.edu)
- Provides an introduction to basic statistical and data analytic methods. (umc.edu)
- Continues introductions to intermediate and advanced statistical analysis methods for biomedical research. (umc.edu)
- This course will emphasize the learning of statistical methods and concepts through hands-on experience with real data. (umc.edu)
- This course introduces basic concepts and methods for analyzing survival time data obtained from following individuals until occurrence of an event or their loss to follow-up. (umc.edu)
- Statistical geneticists initially worked mainly with linear models and other parametric methods. (bcr.org)
- When applied to genetic studies, however, these classic methods typically require too many parameters to be estimated from relatively sparse data. (bcr.org)
- Random forests (RF) is a powerful classification tree approach to finding patterns in data, but, as with classical parametric methods, tends to be more responsive to main effects than interactions when used in genetic studies. (bcr.org)
- Correspondingly, a large number of statistical approaches for detecting gene set enrichment have been proposed, but both the interrelations and the relative performance of the various methods are still very much unclear. (biomedcentral.com)
- We conduct an extensive survey of statistical approaches for gene set analysis and identify a common modular structure underlying most published methods. (biomedcentral.com)
- Third, we present an extensive survey of existing statistical methods for detecting enriched gene sets. (biomedcentral.com)
- He is co-author of Statistical Methods for Reliability Data (Wiley, 1998) and of numerous publications in the engineering and statistical literature and has won many awards for his research. (wiley.com)
- Students selecting the Biostatistics track as their primary emphasis area will be expected to develop new statistical methods to accurately interpret biomedical and population health data. (umc.edu)
- The scope of the book, introductory statistics, is a very useful set of methods in parametric and non-parametric statistics up to logistic regression and survival analysis. (springer.com)
- Brief sections introduce the statistical methods before they are used. (springer.com)
- All methods for data analysis, understanding or visualizing are based on models that often have compact analytical representations (e.g., formulas, symbolic equations, etc. (ucla.edu)
- This is in agreement with literature data, and points to the adequacy of the three methods here proposed. (ispub.com)
- At the same time, agreement between methods when real data are being evaluated, as well as providing sense-making differences between groups of subjects may further support their use. (ispub.com)
- Approximate methods: normal approximations to posterior distributions, Laplace's method for calculating ratios of integrals, Gibbs sampling, finding full conditionals, constrained parameter and missing data problems, graphical models. (qmul.ac.uk)
- For non-parametric methods, there are no formal assumptions for how a response variable is related to the covariables, but strong correlation between response and covariables is necessary for variance reduction. (nih.gov)
- Computations for these methods are straightforward through the application of weighted least squares to fit linear models to the differences between treatment groups for the means of the response variable and the covariables jointly with a specification that has null values for the differences that correspond to the covariables. (nih.gov)
- Bayesian statistical methods start with existing 'prior' beliefs, and update these using data to give 'posterior' beliefs, which may be used as the basis for inferential decisions. (scholarpedia.org)
- The course covers methods for modern multivariate data analysis and statistical learning, including both their theoretical foundations and practical applications. (umich.edu)
- Topics include principal component analysis and other dimension reduction techniques, classification (discriminant analysis, decision trees, nearest neighbor classifiers, logistic partitioning methods, model-based methods), and categorical data analysis. (umich.edu)
- This includes: the theory and practice of testing hypotheses, statistical estimation theory, the basic statistical theory underlying the linear model, an introduction to econometric methods, and the nature of the difficulties which arise in applying statistical procedures to economic research problems. (umich.edu)
- Selected topics in computational statistics including: managing and processing large data sets, parallel and distributed programming, simulation and Monte Carlo methods, interactive statistical methods, and optimization. (umich.edu)
- This course will cover statistical models and methods relevant to the analysis of financial data. (umich.edu)
- Using these criteria, parametric and non-parametric ROC curve estimation methods in two different computer software packages were analyzed. (ispub.com)
- Results Compared to non-parametric methods, parametric methods failed to yield a smooth and convex ROC curve for small sample size. (ispub.com)
- On the other hand, parametric methods showed superiority over non-parametric methods in estimating a smooth and convex ROC curve for large sample sizes. (ispub.com)
- Conclusion Parametric methods for ROC curve estimation is recommended over non-parametric methods for large sample size continuous biomarker data sets but it is conservative for small sample size. (ispub.com)
- Generally, we can classify them into parametric, non-parametric and semi-parametric ROC estimation methods. (ispub.com)
- The non-parametric methods don't require any distributional assumptions of the diagnostic test, while the parametric methods are used when the statistical distribution of the test values is known and has the advantage of producing a smooth ROC curve . (ispub.com)
- Finally, the semi-parametric methods which assume using a non-parametric approach to estimate the distribution of test results in healthy population, but then assume a parametric approach for the distribution of test results in diseased population. (ispub.com)
- Since all biological data tend to be noisy, statistical models and methods are a key element of analysis. (royalsocietypublishing.org)
- Besides, he is interested in statistical consultancy which includes general advice on the application of statistical methods including study design, data analysis and interpretation, and on all statistical aspects of business and academic research. (ac.bd)
- As opposed to popular algorithms such as agglomerative hierarchical cluster ing or k-means which return a single cluster ing solution, Bayesian methods provide a posterior over the space of partitions, allowing one to assess statistical properties, such as uncertainty on the number of cluster s. (warwick.ac.uk)
- The aim of this qualification is to prepare the candidates in totality with methods that can be applied for the gathering and interpretation of data and empirical information. (up.ac.za)
- It is recommended for students with any mathematical or statistical background and those needing a firm foundation in statistical methods either for their careers or preparation for further quantitative courses. (tulane.edu)
- The broader impact of my other work over the previous few years has been to establish both within and outside the field the practical importance of computer-intensive statistical methods, such as the bootstrap, shrinkage estimation, and local false discovery rates. (stanford.edu)
- We introduce two novel statistical methods: for (B) the correlation stability coefficient , and for (C) the bootstrapped difference test for edge-weights and centrality indices. (springer.com)
- Parametric and nonparametric methods. (isikun.edu.tr)
- Comparison of neural approaches with parametric and nonparametric statistical methods. (isikun.edu.tr)
- Introduction to statistical methods for students panning on majoring in health sciences. (uw.edu)
- Introduces regression methods for analysis of continuous, binary, and time-to-event (survival) data. (uw.edu)
- Overview of nonparametric methods, such as rank tests, goodness of fit tests, 2 x 2 tables, nonparametric estimation. (uw.edu)
- Useful for students with only a statistical methods course background. (uw.edu)

- Applications and interpretation of numerical information in context. (jmu.edu)
- Students will learn the core ideas of programming - functions, objects, data structures, flow control, input and output, debugging, logical design and abstraction - through writing code to assist in numerical and graphical statistical analyses. (umc.edu)
- Conversely, statistical shape models (SSM) allow visualisation and analysis of global and regional shape patterns simultaneously and in 3D [ 3 ] as they are constituted by a computational atlas or template , which integrates all anatomical shape information intuitively as a visual and numerical mean shape and its variations in 3D. (biomedcentral.com)
- We develop an efficient algorithm for computing the maximum likelihood solution, demonstrate the effectiveness of the resulting estimator with numerical simulations, and discuss a method of testing the model's validity using time-rescaling and density evolution techniques. (psu.edu)
- The second half of the course will survey tools for handling structured data (regular expressions, HTML/JSON, databases), data visualization, numerical and symbolic computing, interacting with the UNIX/Linux command line, and large-scale distributed computing. (umich.edu)
- Numerical Issues in Statistical Computing for the Social Scientist , 12-43. (stanford.edu)
- The focus will be on numerical computation and interpretation of results of statistical application using statistical packages. (tulane.edu)

- Semi-Parametric Regression Models: The Cox Proportional Hazards Model. (routledge.com)
- The survival analysis chapter, for example, traces the steps from life tables, the Kaplan-Meier estimator, and the Mantel Haenszel log rank test to the proportional hazards model. (stanford.edu)

- Unlike the t-test it does not require the assumption of normal distributions. (wikipedia.org)
- It is nearly as efficient as the t-test on normal distributions. (wikipedia.org)
- Introduces parametric inferencesusing the exponential and Weibull distributions. (jhsph.edu)
- The statistical methodology includes statistical standard distributions, one- and two-sample tests with continuous data, regression analysis, one- and two-way analysis of variance, regression analysis, analysis of tabular data, and sample size calculations. (springer.com)
- To illustrate the method we apply it to standard statistical problems with multinomial distributions. (mdpi.com)

- We hypothesized that changes in the price of a product can influence neural computations associated with EP. (pnas.org)
- To investigate the impact of price on the neural computations associated with EP, we scanned human subjects ( n = 20) using fMRI while they sampled different wines and an affectively neutral control solution, which consisted of the main ionic components of human saliva ( 17 ). (pnas.org)
- Drawing on the technical expertise in theoretical neuroscience and neural network dynamics, along with the expertise in rodent cognition, behavioural modelling, imaging, electrophysiological recordings and optogenetics, we aim to bridge our understanding of memory and (statistical) learning at the behavioural level with its implementation at the circuit and systems level. (ucl.ac.uk)
- The goal is two-fold: to place current neural network approaches to missing data within a statistical framework, and to describe a set of algorithms, derived from the likelihood-based framework, that handle clustering, classification, and function approximation from incomplete data in a principled and efficient manner. (mit.edu)
- We discuss how these data on motion perception fit within the broader literature on perceptual Bayesian priors, perceptual expectations, and statistical and perceptual learning and review the possible neural basis of priors. (frontiersin.org)
- Part 3 concerns 21st century topics, false discovery rates, sparse modeling and the lasso, support vector machines, neural networks, random forests, and other modern data analytic algorithms. (stanford.edu)

- The emphasis will be on applied rather than theoretical statistics, and on understanding and interpreting the results of statistical analyses. (umc.edu)
- Provides an introduction to statistical concepts in the design and analyses of sample surveys. (umc.edu)
- Steps required to set up the statistical shape modelling analyses, from pre-processing of the CMR images to parameter setting and strategies to account for size differences and outliers, are described in detail. (biomedcentral.com)
- Students then conduct independent data analyses for each case study and produce written reports. (umich.edu)
- SUMMARY In this paper we report exploratory analyses of high-density oligonucleotide array data from the Affymetrix GeneChip R system with the objective of improving upon currently used measures of gene expression. (psu.edu)
- The exploratory data analyses of the probe level data motivate a new summary measure that is a robust multiarray average (RMA) of background-adjusted, normalized, and log-transformed P M values. (psu.edu)
- analyses of the robustness of results through the use of statistical tools, such as evaluating the p-curve, replicability index, or using software to test for image manipulation. (stanford.edu)
- Modern microarray analyses depend on a sophisticated data pre-processing procedure called normalization, which is designed to reduce the technical noise level and/or render the arrays more comparable in one study. (rochester.edu)
- These analyses typically involve two steps: (1) estimate a statistical model on data, from which some parameters can be represented as a weighted network between observed variables, and (2), analyze the weighted network structure using measures taken from graph theory (Newman, 2010 ) to infer, for instance, the most central nodes. (springer.com)

- Rich visualization and sophisticated statistics facilitate analysis and interpretation of complex data. (genedata.com)
- Intuitive visualization tools enable explorative data analysis on large data sets and facilitate overview and quality control of MS data (e.g. (genedata.com)
- Introduces fundamental concepts and techniques of survival analysis including censoring, hazard and survival functions, Kaplan-Meier curves and logrank tests. (jhsph.edu)
- The authors illustrate the entire research path required in the application of event-history analysis, from the initial problems of recording event-oriented data to the specific questions of data organization, to the concrete application of available program packages and the interpretation of the obtained results. (routledge.com)
- The Statistical Theory of Event History Analysis. (routledge.com)
- Homework in this course consists of short answer questions to test concepts and guided data analysis problems using software. (statistics.com)
- At the State University of New York at Stony Brook, we are broadly interested in a range of mathematical image analysis algorithms for segmentation, registration, diffusion-weighted MRI analysis, and statistical analysis. (na-mic.org)
- An important problem of TBI neuroimaging data analysis is the task of co-registering MR volumes acquired using distinct sequences in the presence of widely variable pixel intensities that are due to the presence of pathology. (na-mic.org)
- This course represents an introduction to the field and provides a survey of data types and analysis techniques. (umc.edu)
- Provides a basic understanding of the statistical concepts important in the design, conduct and analysis of clinical trials. (umc.edu)
- Analysis of microarray and other high-throughput data on the basis of gene sets, rather than individual genes, is becoming more important in genomic studies. (biomedcentral.com)
- From a statistical point of view the analysis of groups instead of individual genes is advantageous as this typically increases power and reduces the dimensionality of the underlying statistical problem. (biomedcentral.com)
- Overview over statistical algorithms for the analysis of gene set enrichment. (biomedcentral.com)
- From extensive computer simulations and the analysis of two experimental data sets, we offer specific recommendations for conducting an effective gene set analysis. (biomedcentral.com)
- Medical image analysis in clinical practice is commonly carried out on 2D image data, without fully exploiting the detailed 3D anatomical information that is provided by modern non-invasive medical imaging techniques. (biomedcentral.com)
- In this paper, a statistical shape analysis method is presented, which enables the extraction of 3D anatomical shape features from cardiovascular magnetic resonance (CMR) image data, with no need for manual landmarking. (biomedcentral.com)
- The primary objective of the program is to educate students on statistical theory, practical data analysis, big data management and manipulation, and communication to the scientific and general community. (umc.edu)
- In addition, the class works collaboratively to build skills in experimental design and data analysis via readings and class demonstrations/activities. (rochester.edu)
- R is now in widespread use for teaching at many levels as well as for practical data analysis and methodological development. (springer.com)
- For experienced statisticians and data analysts, the book provides a good overview of the basic statistical analysis capabilities of R and presumably prepares readers for later migration to S…The format of this compact book is attractive…The book makes excellent use of fonts and intersperses graphics near the codes that produced them. (springer.com)
- R is thus ideally suited for teaching at many levels as well as for practical data analysis and methodological development. (springer.com)
- There are two important concepts in any data analysis - Population and Sample . (ucla.edu)
- The objective is to produce research-oriented scientists who anticipate a career performing data management and statistical analysis. (uab.edu)
- An interactive tool for data analysis. (mathforum.org)
- Statistical analysis in a format that imports and exports all major spreadsheets, databases, and statistical file formats. (mathforum.org)
- A comprehensive, integrated statistical data analysis, graphics, database management, and custom application development system featuring a wide selection of basic and advanced analytic procedures for science, engineering, business, and data mining applications. (mathforum.org)
- Computer Science (Sci) : An introduction to the design of computer algorithms, including basic data structures, analysis of algorithms, and establishing correctness of programs. (mcgill.ca)
- Issues for covariance analysis of dichotomous and ordered categorical data from randomized clinical trials and non-parametric strategies for addres. (nih.gov)
- The statistical basis of covariance analysis can be either non-parametric, with reliance only on the randomization in the study design, or parametric through a statistical model for a postulated sampling process. (nih.gov)
- Since non-parametric covariance analysis can have many forms, the ones which are planned for a clinical trial need careful specification in its protocol. (nih.gov)
- A limitation of non-parametric analysis is that it does not directly address the magnitude of treatment effects within subgroups based on the covariables or the homogeneity of such effects. (nih.gov)
- In this retrospective analysis, subjects were selected from an ongoing data base of patients who presented to the emergency department of our institution and had confirmed first-time acute ischemic stroke from January 2004 to August 2007. (ajnr.org)
- MFC values across groups were compared by using analysis of covariance, and the relationship of MFC values and neuropsychological tests were evaluated by using Spearman correlations. (ajnr.org)
- While an innocuous theory, practical use of the Bayesian approach requires consideration of complex practical issues, including the source of the prior distribution, the choice of a likelihood function, computation and summary of the posterior distribution in high-dimensional problems, and making a convincing presentation of the analysis. (scholarpedia.org)
- OxMetrics A family of of software packages providing an integrated solution for the econometric analysis of time series, forecasting, financial econometric modelling, or statistical analysis of cross-section and panel data. (oxmetrics.net)
- While there will be some theory, the emphasis will be on applications and data analysis. (umich.edu)
- There will be a significant data analysis component. (umich.edu)
- In addition, using memory items that lie on a quantitative, graded, continuum greatly facilitates both analysis and modeling of the data. (ucl.ac.uk)
- Brief explanations of the use and interpretation of standard statistical analysis techniques. (mathforum.org)
- Citation Query Model-based analysis of oligonucleotide arrays: Expression index computation and outlier detection. (psu.edu)
- Model-based analysis of oligonucleotide arrays: Expression index computation and outlier detection. (psu.edu)
- Change-point and Spatio-temporal Analysis of Climate Data in Bangladesh (with Paritosh K. Roy), Ministry of Science and Technology, Bangladesh Govt. (ac.bd)
- Data were analyzed for differences between groups using analysis of variance and t-tests. (biomedcentral.com)
- After taking the course, students will be able to create databases with applications to public health intervention and surveillance, use SQL to administrate, manage, and retrieve data for statistical analysis. (tulane.edu)
- The advantages of employing 3D fracture mechanics tools were clearly demonstrated when the prediction using the advanced analysis technique produced a crack growth lifetime 10 times greater than the standard 2D tools and well within a factor of two of the test result. (spie.org)
- Multiple regression analysis on ordinal categorical variables was used to test for the simultaneous associations of clinical and microbiological variables on quartiles of cytokine concentrations in lavage samples. (bmj.com)
- A statistical (analysis of variance) method for analysis of molecular genetic data. (tripod.com)
- ANOVA (analysis of variance): A test for significant differences between multiple means by comparing variances. (tripod.com)
- The conceptual framework was tested using Spearman's rank correlation analysis and linear regression analysis. (scielo.org.za)
- J. Polzehl , K. Tabelow , Magnetic resonance brain imaging: Modeling and data analysis using R , Use R! (wias-berlin.de)
- This book discusses the modeling and analysis of magnetic resonance imaging (MRI) data acquired from the human brain. (wias-berlin.de)
- and neuroimaging students wanting to learn about the statistical modeling and analysis of MRI data. (wias-berlin.de)
- Offering a practical introduction to the field, the book focuses on those problems in data analysis for which implementations within R are available. (wias-berlin.de)
- It also includes fully worked examples and as such serves as a tutorial on MRI analysis with R, from which the readers can derive their own data processing scripts. (wias-berlin.de)
- The main chapters cover three common MR imaging modalities and their data modeling and analysis problems: functional MRI, diffusion MRI, and Multi-Parameter Mapping. (wias-berlin.de)
- The same idea can be easily extended to spatial data analysis. (wikipedia.org)

- These algorithms are based on mixture modeling and make two distinct appeals to the Expectation-Maximization (EM) principle (Dempster, Laird, and Rubin 1977)-- -both for the estimation of mixture components and for coping with the missing data. (mit.edu)

- A survey of probability theory and statistical techniques with applications to biological and biomedical situations. (stonybrook.edu)
- The course is the first of a sequence in the theory of statistical interference and probability. (tulane.edu)

- Selection and use of appropriate tools: scientific notation, percentages, descriptive summaries, absolute and relative changes, graphs, normal and exponential population models, and interpretations of bivariate models. (jmu.edu)
- Using an approximate likelihood method and minimum-distance statistics, our estimates of statistical power indicate that exponential and algebraic growth can indeed be distinguished from multiple-merger coalescents, even for moderate sample sizes, if the number of segregating sites is high enough. (genetics.org)
- We show through simulation that our test can discriminate effectively between the presence and absence of recombination, even in diverse situations such as exponential growth (star-like topologies) and patterns of substitution rate correlation. (genetics.org)
- To obtain the local concentration result for the marginal posterior of the lower support (Bernstein - von Mises type theorem), we give a set of conditions on the joint prior, that ensure that the marginal posterior distribution of the lower support point of the density has shifted exponential distribution in the limit, as in the parametric case with known density (Ibragimov and Has'misnkij, 1981). (warwick.ac.uk)

- We first apply this methodology to characterize the LA anatomy of 144 AF patients and build a statistical shape model that includes the most salient variations in shape across this cohort. (frontiersin.org)
- Evaluation is based on attaining insight from the data, effective communication of findings, and appropriate use of statistical methodology, as well as active participation in class discussions. (umich.edu)
- We conduct a simulation study and illustrate our methodology using data from a brain cancer trial. (warwick.ac.uk)
- Introduction to statistical methodology in the health field. (tulane.edu)
- The methodology presented here is the result of 20 years worth of applied research on various large industrial data sets, where the author tried for years (eventually with success) to build a system that is simple and work. (datasciencecentral.com)
- A methodology for automatic damage identification and localization is developed using a combination of vibration and wave propagation data. (spie.org)

- demonstrates, through examples, how to implement hypotheses tests and how to choose the right model. (routledge.com)
- In order to distinguish biological from statistical significance of hypotheses, we propose a new protocol that incorporates measurement error and effect size. (scielo.br)

- A normalized version of the SFS (nSFS) is also used as a summary statistic in an approximate Bayesian computation (ABC) approach. (genetics.org)
- In this paper we review the problem of learning from incomplete data from two statistical perspectives---the likelihood-based and the Bayesian. (mit.edu)
- The intention is to give a Bayesian alternative to significance tests or, equivalently, to p-values. (mdpi.com)
- He is also interested to work in statistical computing, Machine Learning, Bayesian Statistics and Social Statistics. (ac.bd)

- Microarray survival data from patients with diffuse large B-cell lymphoma, in combination with the recent, bootstrap-based prediction error curve technique, is used to illustrate the advantages of the new procedure. (biomedcentral.com)
- A recipient of a 2005 National Medal of Science for his contributions to theoretical and applied statistics, especially the bootstrap sampling technique, in 2014 he was awarded the Guy Medal in Gold by the Royal Statistical Society. (stanford.edu)
- Third, we describe how bootstrap routines can be used to (A) assess the accuracy of estimated network connections, (B) investigate the stability of centrality indices, and (C) test whether network connections and centrality estimates for different variables differ from each other. (springer.com)

- SAS and Stata are the computation softwares used in class. (jhsph.edu)
- Datasets will be analyzed using the statistical package STATA. (umc.edu)
- Course content will be delivered through lectures, hands-on lab instruction and team-based learning using multiple statistical packages (R, SAS and Stata). (umc.edu)
- The course emphasizes hands-on experience, particularly, allowing students to develop a working knowledge and essential programming skills of commonly used statistical packages, such as SAS, R and STATA, for managing and characterizing public health-related data. (tulane.edu)

- Major assumptions of ANOVA are the homogeneity of variances (it is assumed that the variances in the different groups of the design are similar) and normal distribution of the data within each treatment group. (tripod.com)

- Parametric Regression Models. (routledge.com)
- Participants will also be able to fit statistical models to dose-response data, with the goal of quantifying a reliable relationship between drug dosage and average patient response. (statistics.com)
- Covers statistical models for drawing scientific inferences from clustered\correlated data such as longitudinal and multilevel data. (umc.edu)
- The anatomical mean shape of 20 aortic arches post-aortic coarctation repair (CoA) was computed based on surface models reconstructed from CMR data. (biomedcentral.com)
- Based on this template, descriptive or predictive statistical shape models can be built [ 1 , 4 ], to explore how changes in shape are associated with functional changes. (biomedcentral.com)
- Empirical validations of the utility of models are achieved by inputting data and executing tests of the models. (ucla.edu)
- Lecture notes and teaching materials for a course on statistical forecasting, with particular focus on regression and time series models. (mathforum.org)
- While decoding models were able to predict unseen data when trained and tested on the same rule, they were unable to do so when trained and tested on different rules. (jneurosci.org)
- For example, parametric mixed-effects models [ 13 - 15 ] impose strong assumptions on underlying biology mechanisms and might produce coefficients with limited biological relevance [ 16 ], whereas nonparametric mixed-effects models impose no assumptions and may lose useful information when some information is available. (omicsonline.org)
- At our lab, computational models are part of our comparative studies, like any other biological species, in order to systematically inform and constrain the experimental designs and data interpretations, and conversely be constrained by experimental findings. (ucl.ac.uk)
- When predictive survival models are built from high-dimensional data, there are often additional covariates, such as clinical scores, that by all means have to be included into the final model. (biomedcentral.com)
- We introduce a new boosting algorithm for censored time-to-event data that shares the favorable properties of existing approaches, i.e., it results in sparse models with good prediction performance, but uses an offset-based update mechanism. (biomedcentral.com)
- For models built from high-dimensional data, e.g. arising from microarray technology, often survival time is the response of interest. (biomedcentral.com)
- Parametric hazard models are used to test whether changes in consumer sentiments about the state of the economy Granger-cause changes in cyclical durations. (thefreelibrary.com)
- Non-parametric k NN models were developed to estimate W t and SOC. (mdpi.com)
- High-dimension data demand high-dimensional models with ten to hundreds of thousands of parameters. (pubmedcentralcanada.ca)
- Fundamental concepts of data modeling and popular data models. (isikun.edu.tr)
- The method makes use of linear state-space (SS) models to provide the multiscale parametric representation of an AR process observed at different time scales and exploits the SS parameters to quantify analytically the complexity of the process. (hindawi.com)
- To test for group differences in growth trajectories in mixed (fixed and random-effects) models, researchers frequently interpret the coefficient of group-by-time product terms. (stata.com)
- While this practice is straightforward in linear mixed models, testing for group differences in generalized linear mixed models is more complex. (stata.com)
- Using both an empirical example and simulated data, we show that the coefficient of group-by-time product terms in mixed logistic and Poisson models estimate the multiplicative change with respect to the baseline rates, while researchers often are more interested in differences in the predicted rate of change between groups. (stata.com)

- will sort of describe the approaches to analyzing large data sets. (coursera.org)
- The course focuses on the theoretical underpinnings of biostatistics and improving understanding of statistical application and problem solving approaches. (tulane.edu)
- Two approaches to evaluating splits of the data are examined. (biomedcentral.com)

- Certificate - You may be enrolled in PASS (Programs in Analytics and Statistical Studies) that requires demonstration of proficiency in the subject, in which case your work will be assessed for a grade. (statistics.com)
- Students completing the Data Science track will be able to create systems to turn vast amounts of data into actionable evidence, requiring additional knowledge in computer science, data mining, applied mathematics, predictive analytics, and data visualization. (umc.edu)
- Data analytics and other consulting services. (mathforum.org)
- Our course finder pages contain all the most up-to-date information about the Data Analytics MSc, including details of the programme structure, compulsory and elective modules and study options. (qmul.ac.uk)
- This module is offered to allow you to move beyond the basic techniques of Machine Learning, and is a core component of the MSc Data Analytics. (qmul.ac.uk)

- One is reduction of variance for estimates of treatment effects and thereby the production of narrower confidence intervals and more powerful statistical tests. (nih.gov)
- We display some familiar features of the perfect match and mismatch probe (P M and M M) values of these data, and examine the variance-mean relationship with probe-level data from probes believed to be defective, and so delivering noise only. (psu.edu)
- We evaluate the four expression summary measures using the dilution study data, assessing their behavior in terms of bias, variance and (for MBEI and RMA) model fit. (psu.edu)
- StatsToDo provides three commonly used tests for homogeneity of variance. (statstodo.com)
- AMOVA produces estimates of variance components and F-statistic analogs (designated as phi-statistics). (tripod.com)
- The significance of the variance components and phi-statistics is tested using a permutational approach, eliminating the normality assumption that is inappropriate for molecular data ( Excoffier, 1992 ). (tripod.com)

- He applies these methodological tools to the modelling and control of telecommunication systems and to design data mining and machine learning algorithms. (tkk.fi)
- Finally, we evaluate the algorithms in terms of their ability to detect known levels of differential expression using the spike-in data. (psu.edu)
- To illustrate the performance of the MM algorithms, we compare them to Newton's method on data used to classify handwritten digits. (pubmedcentralcanada.ca)

- There are two important ways to describe a data set (sample from a population) - Graphs or Tables . (ucla.edu)
- StatiBot will perform appropriate statistical tests, comment on test results, and create graphs. (mathforum.org)

- After the calculation of parametric maps, MFC was measured by using a region of interest approach. (ajnr.org)

- Our second approach involves development of a non-parametric method that does not rely on distributional assumptions and can be applied directly to any existing dataset without stipulating any parameter values. (biomedcentral.com)

- Students analyze real data sets using standard statistical software, interpret the output, and write extensively about the results. (stonybrook.edu)
- Standard statistical techniques presented with examples drawn from the health sciences literature. (uw.edu)

- Method one: For comparing two small sets of observations, a direct method is quick, and gives insight into the meaning of the U statistic, which corresponds to the number of wins out of all pairwise contests (see the tortoise and hare example under Examples below). (wikipedia.org)
- The main mode of presentation is via code examples with liberal commenting of the code and the output, from the computational as well as the statistical viewpoint. (springer.com)
- Examples with measurement data for species of the frog genus Leptodactylus are presented. (scielo.br)
- Mathematics & Statistics (Sci) : Examples of statistical data and the use of graphical means to summarize the data. (mcgill.ca)
- For most of our examples, the derivation of a corresponding EM algorithm appears much harder, the main hindrance being the difficulty of choosing an appropriate missing data structure. (pubmedcentralcanada.ca)

- As no underlying assumptions are made concerning the origin of the sequences, these tests can be applied to detect recombination within any set of aligned homologous sequences. (genetics.org)

- In modern language and notation, Bayes wanted to use Binomial data comprising \(r\) successes out of \(n\) attempts to learn about the underlying chance \(\theta\) of each attempt succeeding. (scholarpedia.org)

- Design of experiments is the blueprint for planning a study or experiment, performing the data collection protocol and controlling the study parameters for accuracy and consistency. (ucla.edu)
- We describe the maximum likelihood estimator for the model parameters, given only extracellular spike train responses (not intracellular voltage data). (psu.edu)
- I will also talk about asymptotic normality for the estimators of the parametric components and variable selection procedures for the linear parameters by employing a nonconcave penalized likelihood, which is shown to have an oracle property. (rochester.edu)
- However, in the limited sample size psychological research typically has to offer, the parameters may not be estimated accurately, and in such cases, interpretation of the network and any measures derived from the network is questionable. (springer.com)
- 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)

- We use this framework to conduct a computer simulation comparing 261 different variants of gene set enrichment procedures and to analyze two experimental data sets. (biomedcentral.com)
- Given this extensive literature, biologists are now confronted with the difficult choice of a gene set method that is best suited to analyze their data at hand. (biomedcentral.com)
- analyze these data in a clear and rigorous manner. (coursera.org)
- Statistical Intervals: A Guide for Practitioners and Researchers, Second Edition is an up-to-date working guide and reference for all who analyze data, allowing them to quantify the uncertainty in their results using statistical intervals. (wiley.com)
- Students completing the Bioinformatics & Genomics track will be equipped to analyze a broad range of biological data (including genomics, transcriptomics, proteomics, metabolomics, and epigenomics) to investigate the molecular and environmental basis of human health traits and diseases. (umc.edu)
- More recently, Liang and Sha [ 9 ] applied a parametric nonlinear mixedeffects model [ 10 , 11 ] to analyze changes in tumor volume. (omicsonline.org)

- The purpose of this paper is to give an overview of current statistical applications in genomics, primarily the study of genomes at the DNA and mRNA (transcription) levels. (royalsocietypublishing.org)

- While the course emphasizes interpretation and concepts, there are also formulae and computational elements such that upon completion, class participants have gained real world applied skills. (umc.edu)
- Brett McKinney, PhD , of the University of Tulsa's Institute for Bioinformatics and Computational Biology in Oklahoma has developed an approach called Evaporative Cooling that balances interactions (from Relief-F) and main effects (from RF) in a statistical thermodynamics framework. (bcr.org)
- A project designed to improve the accuracy of statistical software by providing reference datasets with certified computational results that enable the objective evaluation of statistical software. (mathforum.org)
- Specified criteria such as likelihood ratio test, ease of use and computational time were used for evaluation. (ispub.com)
- The resulting linear MSE (LMSE) measure is first tested in simulations, both theoretically to relate the multiscale complexity of AR processes to their dynamical properties and over short process realizations to assess its computational reliability in comparison with RMSE. (hindawi.com)

- Data, or information, is typically collected in regard to a specific process or phenomenon being studied to investigate the effects of some controlled variables (independent variables or predictors) on other observed measurements (responses or dependent variables). (ucla.edu)
- Each of these may generate data of two major types - Quantitative or Qualitative measurements. (ucla.edu)
- The advantage of this approach is that the mapping between signal characteristics and relevance can be derived from preliminary measurements such as Pencil-Lead Break (PLB) test. (ndt.net)
- Nominal scales were often called qualitative scales, and measurements made on qualitative scales were called qualitative data. (wikipedia.org)
- If the measurements are continuous and normally distributed, the powerful parametric statistical procedures can be used. (statstodo.com)
- If the measurements are continuous, but not normally distributed, some form of transformation may be needed before parametric statistical procedures can be used. (statstodo.com)
- If the measurements are not continuous, or if they are not normally distributed and cannot be transformed, then the nonparametric statistical procedures can be used. (statstodo.com)
- If the data are not measurements, such as counts or classifications, then they cannot be analysed as measurements. (statstodo.com)

- To exploit jointly the information contained in the acquired video sequence and the data provided by the INS, a specific detection and tracking algorithm has been developed. (spiedigitallibrary.org)
- The algorithm has been tested on a large dataset of simulated IR video sequences, recreating different environments and different movements of the aircraft. (spiedigitallibrary.org)
- The algorithm requires no training, is adaptive, demonstrating good performance for differing data types including CT and MRI, and requires minimal user input. (spie.org)
- We develop a non-parametric algorithm for determining an optimal splitting proportion that can be applied with a specific dataset and classifier algorithm. (biomedcentral.com)

- This paper summarizes a spin test of an IN100 minidisk that demonstrated advanced fatigue crack growth predictive tools under dwell fatigue and the ability to infer the damage state from state-awareness sensed data. (spie.org)
- We consider the problem of designing a study to develop a predictive classifier from high dimensional data. (biomedcentral.com)

- The Doctor of Philosophy (PhD) program in Biostatistics & Data Science will prepare graduates to conduct cutting-edge research, teach the next generation of biostatisticians and data scientists, and collaborate with basic research scientists, clinicians, epidemiologists, and population and public health organizations. (umc.edu)
- Enrolled students will be able to complete the doctoral program in 5 years, earning a total of 60 credit hours and a master of science (MS) in Biostatistics & Data Science along the way. (umc.edu)

- Under this location shift assumption, we can also interpret the Mann-Whitney U test as assessing whether the Hodges-Lehmann estimate of the difference in central tendency between the two populations differs from zero. (wikipedia.org)

- The equivalent of Introduction to Statistical Issues in Clinical Trials . (statistics.com)
- Possibilities include epidemiological data, randomised clinical trials, radiocarbon dating. (qmul.ac.uk)
- Imaging and clinical data obtained as part of standard clinical stroke care at our institution were retrospectively reviewed. (ajnr.org)
- Raw data from clinical trials: within reach? (stanford.edu)
- Addresses hospital statistics, used to calculate usage levels of heathcare resources and outcomes of clinical operations, and research statistics, used to summarize and describe significant characteristics of a data set, and to make inferences about a population based on data collected from a sample. (uw.edu)

- The book concludes with extended appendices providing details of the non-parametric statistics used and the resources for R and MRI data.The book also addresses the issues of reproducibility and topics like data organization and description, as well as open data and open science. (wias-berlin.de)

- Its greatest usefulness is probably in a course for graduate students of applied statistics….the classical standard packages remain an important tool for many analysts, who are bound to find this text very helpful as a work of reference when they set up their computations. (routledge.com)
- These or each of these tests can is taught in standard statistics courses. (coursera.org)
- Or take a statistics course to understand how these tests can be used. (coursera.org)
- This program synergizes competencies in statistics, computer science, and epidemiology, a critical combination of skills for analyzing increasingly complex health-related data. (umc.edu)
- The phrase Uses and Abuses of Statistics refers to the notion that in some cases statistical results may be used as evidence to seemingly opposite theses. (ucla.edu)
- The exam will test the students on their understanding and comprehension of the foundation of the theory and applications of statistics, and will generally cover materials from BST 621, 622, 623, 626 and 655. (uab.edu)
- THE site-frequency spectrum (SFS) at a given locus is one of the most important and popular statistics based on genetic data sampled from a natural population. (genetics.org)
- This course provides students with hands-on experience using a variety of techniques from modern applied statistics through case studies involving data drawn from various fields. (umich.edu)
- This course is restricted to Master in Applied Statistics and Masters in Data Science students only. (umich.edu)

- The posterior distribution is a formal compromise between the likelihood, summarizing the evidence in the data alone, and the prior distribution, which summarizes external evidence which suggested higher rates. (scholarpedia.org)

- There are two outstanding problems when evaluating sexual dimorphism in measurement variables in frogs: (1) large measurement error, and (2) statistical versus biological significance. (scielo.br)
- Biological interpretation was also discussed. (omicsonline.org)
- In §3, we introduce various types of modern biological technologies, the data being generated and the biological questions that are being posed. (royalsocietypublishing.org)
- Increased rates of both bipolar and unipolar illness are seen in biological parents but not in adoptive parents of bipolar adoptees, indicating that the family and twin data indeed reflect the action of genes (49). (acnp.org)

- The test of significance in psychological research. (stanford.edu)
- The case against statistical significance testing. (stanford.edu)

- Real-world learning tasks often involve high- dimensional data sets with complex patterns of missing features. (mit.edu)

- We also found that model-derived shape metrics, such as the anterior-posterior radius, were better predictors than equivalent metrics taken directly from MRI or echocardiography, suggesting that the proposed approach leads to a reduction of the impact of data artifacts and noise. (frontiersin.org)
- For this purpose, a statistical model is needed. (nih.gov)
- We then examine the behavior of the P M and M M using spike-in data and assess three commonly used summary measures: Affymetrix's (i) average difference (AvDiff) and (ii) MAS 5.0 signal, and (iii) the Li and Wong multiplicative model-based expression index (MBEI). (psu.edu)
- Multiple response Gaussian processes emulate the model response surface and its discrepancy enhancing the identification task while minimising costly computations. (ndt.net)
- To identify a model from the AE data Platt calibration is used, which was developed to map the output of support vector machines to probabilities. (ndt.net)
- We also fit the same outcome model when in addition the latent variable is assumed to be a parametric function of three distinct socioeconomic measures. (rochester.edu)
- We constructed an MCMC sampler for this prior, and its performance is illustrated on simulated data and applied to model distribution of bids in procurement auctions. (warwick.ac.uk)
- Like many other researchers, we work with non-model organisms for which there is no transcriptome, genomic, or proteomic data. (hupo.org)
- This model contains both raster and geometric data. (spie.org)

- While prior publications have tackled the topics of estimating and interpreting such networks, little work has been conducted to check how accurate (i.e., prone to sampling variation) networks are estimated, and how stable (i.e., interpretation remains similar with less observations) inferences from the network structure (such as centrality indices) are. (springer.com)

- The test of association involves calculating the differences between the observed and expected frequencies. (biomedcentral.com)
- thus dichotomous data involves the construction of classifications as well as the classification of items. (wikipedia.org)

- The simplest case is utilization of computer algebra systems like SageMath, Mathematica, Maple, that enables execution of huge amounts of symbolic computations. (unich.it)

- The process of inferring statistical patterns and priors constitutes the foundation of further cognitive abilities. (ucl.ac.uk)
- In our lab, we employ a synergistic combination of theory and experiment to study the fundamental principles by which the nervous system computes, represents and integrates various forms of sensory memories and priors in the process of learning and inferring meaningful statistical patterns and abstract relations in the environment. (ucl.ac.uk)

- A common feature of these activities is the generation of enormous amounts of complex data, which, as is common in science, though gathered for the study of one group of questions, can be fruitfully integrated with other types of data to answer additional questions. (royalsocietypublishing.org)
- The advanced techniques in question are math-free, innovative, efficiently process large amounts of unstructured data, and are robust and scalable. (datasciencecentral.com)

- Abstract interpretation, for instance, is a field where geometrical objects in the configuration space of the variables of a program are used to prove that the program is correct [7, (unich.it)
- Automatic generation of polynomial invariants of bounded degree using abstract interpretation. (unich.it)
- Abstract interpretation based on detonational and operational semantics. (isikun.edu.tr)

- It is used to determine a cutoff value for that specific diagnostic test giving the optimal sensitivity and specificity, which is a point at which one can differentiate between two statuses (healthy and diseased). (ispub.com)
- This paper reports a new method for detecting optimal boundaries in multidimensional scene data via dynamic programming (DP). (spie.org)

- Provides an introduction to selected important topics in statistical concepts and reasoning. (umc.edu)
- Provides an introduction to programming and data management. (umc.edu)
- Statistical Programming with R. This course will provide students with an introduction to statistical computing. (umc.edu)
- Introduction Sensitivity and specificity are two components that measure the performance of a diagnostic test. (ispub.com)
- An introduction to the principles and application of data management, techniques in data collection, data cleaning, data reporting, database design, and implementing databases for managing large data systems. (tulane.edu)
- Our goal is to review the development of statistical thinking since the introduction of electronic computation in the 1950s. (stanford.edu)
- Introduces the field of forensic genetics through discussion of genetic and statistical issues emerging since the introduction of DNA profiling. (uw.edu)
- The book starts with a short introduction to MRI and then examines the process of reading and writing common neuroimaging data formats to and from the R session. (wias-berlin.de)