In this project I explain the theory behind Ridge regression from a Bayesian perspective and suggest why one might use Ridge regression over classical methods. Then, using a data set on Diabetes I constructed a series of classical and Ridge models and compared their effectiveness, including an extension to a selection of ‘hybrid’ models. I found that, for this data set, the classical subset models were better at the prediction of new data than the Ridge models, but suggest situations in which the Ridge models may be preferable. I also derive the analytical posteriors of the parameters in the appendix.

This project formed part of my Masters in Statistics.