Regularization Methods and High Dimensional Data: A Comparative Study Based on Frequentist and Bayesian Methods
As the amount of high dimensional data becomes increasingly accessible and common, the need for reliable methods to combat problems such as overfitting and multicollinearity increases. Models need to be able to manage large data sets where predictor variables often outnumber the amount of observations. In this study the frequentist and Bayesian framework is tested against each other based on three