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Course Criteria
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3.00 Credits
(Same as STATS 352.) Statistical descriptions of spatial variability, spatial random functions, grid models, spatial partitions, spatial sampling, linear and nonlinear interpolation and smoothing with error estimation, Bayes methods and pattern simulation from posterior distributions, multivariate spatial statistics, spatial classification, nonstationary spatial statistics, space-time statistics and estimation of time trends from monitoring data, spatial point patterns, models of attraction and repulsion. Applications to earth and environmental sciences, meteorology, astronomy, remotesensing, ecology, materials. GER:DB-Math 3 units, Spr (Taylor, J)
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3.00 Credits
Use of correspondence analysis (CA) method for dimensionreduction based on the singular-value decomposition, aimed at frequency data or raw multivariate categorical observations. Comprehensive treatment of simple and multiple CA and related methods, using R packages and including 2- and 3-dimensional graphics. 3 units, Aut (Staff)
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1.00 - 2.00 Credits
(Same as HRP 260A.) Applications of statistical techniques to current problems in medical science. 1-2 units, Aut (Olshen, R)
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1.00 - 2.00 Credits
(Same as HRP 260B.) Applications of statistical techniques to current problems in medical science. 1-2 units, Win (Olshen, R)
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1.00 - 2.00 Credits
(Same as HRP 260C.) Applications of statistical techniques to current problems in medical science. 1-2 units, Spr (Olshen, R)
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3.00 Credits
(Same as BIOMEDIN 233, HRP 261.) Methods for analyzing data from case-control and cross-sectional studies: the 2x2 table, chisquare test, Fisher's exact test, odds ratios, Mantel-Haenzel methods, stratification, tests for matched data, logistic regression, conditional logistic regression. Emphasis is on data analysis in SAS. Special topics: cross-fold validation and bootstrap inference. 3 units, Win (Sainani, K)
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3.00 Credits
(Same as HRP 262.) Methods for analyzing longitudinal data. Topics include Kaplan-Meier methods, Cox regression, hazard ratios, timedependent variables, longitudinal data structures, profile plots, missing data, modeling change, MANOVA, repeated-measures ANOVA, GEE, and mixed models. Emphasis is on practical applications. Prerequisites: basic ANOVA and linear regression. 3 units, Spr (Sainani, K)
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3.00 Credits
(Same as STATS 370.) Bayesian statistics including theory, applications, and computational tools. Topics: history of Bayesian methods, foundational problems (what is probability), subjective probability and coherence, exchangeability and deFinetti's theorem. Conjugate priors, Laplace approximations, Gibbs sampling, hierarchical and empirical Bayes, nonparametric methods, Dirichlet and Polya tree priors. Bayes robustness, asymptotic properties of Bayes procedures. 3 units, Win (Wong, W)
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3.00 Credits
For Statistics graduate students and others whose research involves data analysis and development of associated computational software. Programming and computing techniques to support projects in data analysis and related research. Prerequisites: CS 106, and STATS 110 or 141, or equivalent background. 3 units, Win (Narasimhan, B; Chambers, J)
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3.00 Credits
For students in the M.S. program in Financial Mathematics only. Students obtain employment in a relevant industrial or research activity to enhance their professional experience. May be repeated for credit once. Prerequisite: consent of adviser. 1-3 units, Aut (Lai, T), Win (Lai, T), Spr (Lai, T), Sum (Lai, T)
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