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Course Criteria
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3.00 Credits
Includes the origins of generalized linear models, classical linear models, probit analysis, logit models for proportions, log-linear models for counts, inverse polynomial models, binary data, polytomous data, quasi-likelihood models, and models for survival data.
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3.00 Credits
Includes multivariate normal distributions, maximum likelihood inference, invariance theory, sample correlation coefficients, Hotelling’s T2 statistic, Wishart distributions, discriminant analysis, and MANOVA.
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3.00 Credits
Includes order statistics, distribution-free statistics, U-statistics, rank tests and estimates, asymtotic efficiency, Bahadur efficiency, M-estimates, one- and two-way layouts, multivariate location models, rank correlation, and linear models.
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3.00 Credits
An introduction to the design and analysis of sample surveys. Topics include simple random sampling, stratified sampling, multistage (cluster) sampling, double sampling, ratio and regression estimates. Theoretical discussions are supplemented by computer simulated surveys, and studies of the documentation of ongoing government sample surveys.
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3.00 Credits
Studies computational methods for multiple linear regression, unconstrained optimization and non-linear regression, model-fitting based on Lp norms, and robust estimation.
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3.00 Credits
The course will emphasize those techniques which are important for the applied statistician: various forms of convergence for random variables, central limit theorems, asymptotics for a transformation of a sequence of random variables, and an introduction to martingales.
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3.00 Credits
Review of matrix theory (various types of generalized inverses and their properties). Theory and analysis of fixed effects linear models. Estimation of variance components in random and mixed effects linear models. Various methods of estimation of variance components such as: Henderson’s three methods, MLE, RMLE, MINQUE (and its modifications). Theory and analysis of random and mixed effects models.
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3.00 Credits
Martingales, martingale central limit theorem with applications such as counting process survival analysis, time series, and financial models. (E)
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3.00 Credits
Includes modern computer-intensive methods of data analysis, including splines and other methods of nonparametric regression, bootstrap, techniques for handling missing values and data reduction, nonlinear regression, graphical techniques, and penalized maximum likelihood estimation.
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1.00 - 3.00 Credits
Introduces the practice of statistical consultation. A combination of formal lectures, meetings with clients of the statistical consulting service, and sessions in the statistical computing laboratory.
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