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Course Criteria
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3.00 Credits
Parametric models; Kaplan-Meier estimator; nonparametric estimation of survival and cumulative hazard functions; log-rank test; Cox model; Stratified Cox model; additive hazards model partial likelihood; regression diagnostics; multivariate survival data. Prerequisite: STAT 3500, STAT 7070, STAT 4710/7710 or STAT 4760/7760 or consent of instructor.
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3.00 Credits
Repeated measurements; event history studies; linear and nonlinear mixed effects models; growth models; marginal mean and rate models; pattern-mixture models; selection models; non-informative and informative drop-out; joint analysis of longitudinal and survival data. Prerequisite: STAT 3500, STAT 7070, STAT 4710/7710, or STAT 4760/7760 or instructor's consent.
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3.00 Credits
Random variables; Point estimation; Multiple t-test; Likelihood principle; Analysis of variance; Probabilistic methods for sequence modeling; Gene expression analysis; Protein structure prediction; Genome analysis; Hierarchical clustering and Gene classification. Prerequisite: STAT 3500, 7070, 4710/7710, 4760/7760, or instructor's consent.
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3.00 Credits
Introduction to applied linear models including regression (simple and multiple, subset selection, estimation and testing) and analysis of variance (fixed and random effects, multifactor models, contrasts, multiple testing). No credit toward a graduate degree in statistics. Prerequisite: STAT 3500, 7070, 4710/7710, 4760/7760, or instructor's consent.
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3.00 Credits
Study of analysis of variance and related modeling techniques for cases with fixed, random, and mixed effects. Exposure to designs other than completely randomized designs including factorial arrangements, repeated measures, nested, and unequal sample size designs. Prerequisite: STAT 3500, 7070, 4710/7710, 4760/7760, or instructor's consent.
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3.00 Credits
Examination and analysis of modern statistical techniques applicable to experimentation in social, physical or biological sciences. Prerequisite: STAT 4530/7530 or instructor's consent.
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3.00 Credits
Testing mean vectors; Discriminant analysis; Principal components; Factor analysis; Cluster analysis; Structural equation modeling; Graphics. Prerequisite: STAT 3500, 7070 4710/7710 or 4760/7760 or instructor's consent. No credit towards a graduate degree in statistics.
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3.00 Credits
Introduction to spatial random processes, spatial point patterns, kriging, simultaneous and conditional autoregression, and spatial data analysis. Prerequisite: STAT 4510 or instructor's consent. Recommended: basic knowledge of calculus and matrices.
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3.00 Credits
Bayes formulas, choices of prior, empirical Bayesian methods, hierarchial Bayesian methods, statistical computation, Bayesian estimation, model selection, predictive analysis, applications, Bayesian software. Prerequisite: STAT 3500 or 4510/7510 or instructor's consent.
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3.00 Credits
Introduction to theory of probability and statistics using concepts and methods of calculus. Prerequisite: MATH 2300 or instructor's consent. No credit for MATH4315.
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