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STAT 925: Multivariate Analysis:Methods
3.00 Credits
University of Pennsylvania
Staff. Prerequisite(s): STAT 511 or equivalent. Tests on mean vector, discriminant analysis, multivariate analysis of variance, canonical correlation, principal components, and factor analysis.
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STAT 925 - Multivariate Analysis:Methods
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STAT 926: Multivariate Analysis:Theory
3.00 Credits
University of Pennsylvania
Staff. Prerequisite(s): STAT 551 and 925. Wishart distribution, classification theory, Bayesian inference, and the multivariate general linear model.
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STAT 926 - Multivariate Analysis:Theory
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STAT 927: Bayesian Statistical Theory and Methods
3.00 Credits
University of Pennsylvania
Zhao. Prerequisite(s): STAT 551. A course in Bayesian statistical theory and methods. Axiomatic developments of utility theory and subjective probability, and elements of Bayesian theory.
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STAT 927 - Bayesian Statistical Theory and Methods
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STAT 932: Survival Models and Analysis Methods for Medical and Biological Data
3.00 Credits
University of Pennsylvania
Zhao. Prerequisite(s): STAT 551. Parametric models, nonparametric methods for one-and two-sample problems, proportional hazards model, inference based on ranks. Problems will be considered from clinical trials, toxicology and tumorigenicity studies, and epidemiological studies.
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STAT 932 - Survival Models and Analysis Methods for Medical and Biological Data
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STAT 933: Analysis of Categorical Data
3.00 Credits
University of Pennsylvania
Rosenbaum. Prerequisite(s): STAT 541 and 551. Likelihood equations for log-linear models, properties of maximum likelihood estimates, exact and approximate conditional inference, computing algorithms, weighted least squares methods, and conditional independence and log- linear models. Applied topics, including interpretation of log-linear and logit model parameters, smoothing of tables, goodness-of-fit, and incomplete contingency tables.
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STAT 933 - Analysis of Categorical Data
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STAT 940: Advanced Inference I
3.00 Credits
University of Pennsylvania
Staff. Prerequisite(s): STAT 551. The topics covered will change from year to year. Typical topics include sequential analysis, nonparametric function estimation, robustness, bootstrapping and applications decision theory, likelihood methods, and mixture models.
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STAT 940 - Advanced Inference I
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STAT 941: Advanced Inference II
3.00 Credits
University of Pennsylvania
Staff. Prerequisite(s): STAT 940. A continuation of STAT 940.
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STAT 941 - Advanced Inference II
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STAT 950: Statistical Practice I
3.00 Credits
University of Pennsylvania
Staff. Prerequisite(s): STAT 540, 541, 550, and 551. Students will be exposed to the conceptual and practical difficulties of actual statistical practice. Each student will be assigned to work on one or more applied problems arising in the Statistical Consulting Laboratory.
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STAT 950 - Statistical Practice I
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STAT 951: Statistical Practice II
3.00 Credits
University of Pennsylvania
Staff. Prerequisite(s): STAT 540, 541, 550 and 551. A continuation of STAT 950.
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STAT 951 - Statistical Practice II
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STAT 953: Bioinformatics
3.00 Credits
University of Pennsylvania
Ewens. Prerequisite(s): Good background in probability and statistics at the approximate level of STAT 430 and STAT 431. The material will follow the class textbook, Ewens and Grant "Statistical Models in Bioinformatics", Springer, second edtion, 2005. An introduction to the use of statistical methods in the increasingly important scientific areas of genomics and bioinformatics. The topics to be covered will be decided in detail after the initial class meeting, but will be taken from the following: - background probability theory of one and many random variables and of events; background statistical inference theory, classical and Bayesian; Poisson processes and Markov chain; the analysis of one and many DNA sequences, in particular shotgun sequencing, pattern analysis and motifs; substitution matrices, general random walk theory, advanced statistical inference, the theory of BLAST, hidden Markov models, microarray analysis, evolutionary models.
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STAT 953 - Bioinformatics
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