This paper provides an alternative approach to penalized regression for model selection in the context of high-dimensional linear regressions where the number of covariates is large, often much larger ...

First, multiple linear regression models are considered and the design matrices are allowed to be different. Second, the predictor variables are either unconstrained or constrained to finite intervals ...

In this module, we will introduce generalized linear models (GLMs) through the study of binomial data. In particular, we will motivate the need for GLMs; introduce the binomial regression model, ...