Robust Ridge regression to solve a multicollinearity and outlier

Abstract

Regression analysis is one of many methods used for analysing data. Method that used for estimating parameter in linear regression model is ordinary least square (OLS). OLS will give best estimator when all the assumptions are met. But in reality, sometimes not all the assumptions are met. Assumptions that usually violated are multicollinearity and outlier. Ridge regression is a regression method that give constrain on the parameters that used to deal with multicollinearity, meanwhile Robust regression is used to overcome the presence of outlier. Robust regression is a regression method that has robust property that achieved by using S-estimation is used. Ridge regression and Robust regression combined into Robust Ridge regression to overcome multicollinearity and outlier simultaneously.