Advanced Statistical Modeling

In this course students will be introduced to the practical application of advanced linear models for those who have completed work in Regression and Statistical Inference. Building on the foundation of linear model theory, we will explore how relaxing traditional assumptions of the error distribution (e.g., normality, independence, homoscedasticity) leads to new classes of models and estimation methods. Topics include generalized linear models, mixed models, spatial models, generalized least squares, restricted maximum likelihood, and Bayesian estimation. Students will work independently and in small groups to understand and demonstrate application of advanced statistical methods, culminating in a written paper and oral presentation. This course fulfills the capstone senior work requirement for the statistics major.