Variable Selection

Regression models describingthe dependence between a univariate response and a set of covariates play a fundamental role in statistics. In the last two decades, a tremendous effort has been made in developing flexible regression techniques such as generalized additive models(GAM…

This dissertation is composed of three papers which address the problem of variable selection for models with missing data. In the first paper, we consider variable selection for generalized linear models with missing data, including missing covariate and/or response data. The s…

We propose to extend the use of penalized likelihood based variable selection methods to hierarchical generalized linear models (HGLMs) for jointly modellingboth the mean and variance structures. We are interested in applying these newmethods on multilevel structured data, hence…