Generalized linear models including logistic and Poisson regression (hetrogeneous vairance structure, quasi-likelihood), linear mixed-effects models (estimation of variance components, maximum likelihood estimation, restricted maximum likelihood, generalized estimating equations), generalized linear mixed-effects models for discrete data, models for longitudinal data, and optional multivariate models as time permits. The computer software R will be used for examples and homework problems. Implementation in SAS will be mentioned for several specialized models. Prerequisites: Math 5071 and a course on mathematical statistics at the level of Math 494 (can be taken concurrently), or permission of instructor.
Section 01Advanced Linear Models II
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