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Course Criteria
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2.00 Credits
A study of robust and rank-based procedures for estimation and testing in one-and two-sample location problems and linear models. Topics may include; norm-based inference; asymptotic theory; asymptotic relative efficiency; evaluating robustness via the influence function and breakdown; R-estimates, M-estimates, U-statistics.
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2.00 Credits
Statistical models based on the exponential family of distributions where a function of the mean response is linear in the covariates. Applications to non-normal and discrete data, including binary, Poisson and gamma regression, and log-linear models. Topics include likelihood-based estimation and testing, model-fitting, residual analysis, over-dispersed models, quasi-liklihood, and the use of computer packages.
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2.00 Credits
Statistical analysis of spatially and temporally correlated data, including inference for continuous and discrete data based on linear, nonlinear and generalized linear models and methods. Inferential objectives include prediction of response and estimation of correlation/covariance structures.
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2.00 Credits
Application, theory, and computational aspects of resampling methods. Topics include parametric, nonparametric, jackknife, and finite-population resampling; bootstrap confidence intervals and hypothesis tests; randomization theory and permutation tests; applications to regression; implementation using statistical software. Additional topics may include double bootstrap, dependent data, efficient resampling.
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3.00 Credits
Incomplete block designs; theory of the construction and analysis of experimental designs.
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3.00 Credits
The multivariate normal distribution, the Wishart distribution, Jacobians of vector and matrix transformations, Hotelling’s T2statistic, the union-intersection principle, tests on mean vectors and covariance matrices, Box’s approximations to critical points, the multivariate general linear model, discriminant analysis, and principal component analysis.
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0.00 Credits
Principles and practices of statistical consulting. Supervised experience in consultation and consequent research concerning applied statistics and probability associated with on-campus investigations.
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0.00 Credits
Theoretical studies of advanced topics in probability, decision theory, Markov processes, experimental design, stochastic processes, or advanced topics.
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