A numerical study of penalized regression

Abstract

In this thesis, we review important aspects and issues of multiple linear regression, in particular on the problem of multi-collinearity. The focus is on a numerical study of different methods of penalized regression, including the ridge regression, lasso regression and elastic net regression, as well as the newly introduced correlation adjusted regression and correlation adjusted elastic net regression. We compare the performance and relative advantages of these methods.