Course: Model Selection in R

This course focuses on model selection techniques for linear and generalised linear regression in two scenarios: when an extensive search of the model space is possible as well as when the dimension is large and either stepwise algorithms or regularization techniques have to be employed to identify good models. We incorporate recent research on graphical tools for model choice and on how to tune regularisation procedures, such as the Lasso through resampling or model selection criteria. Importantly, the limitations of the various model selection procedures will be discussed.

The practical implementation of the discussed methods is an essential component of this course. Interactive labs will give participants the opportunity to apply what they have learnt. We will use the cross-platform, open-source software R, in particular the `leaps', `bestglm', `glmnet' and `mplot' packages.