Behavior of Residuals in Cook’s Distance for Beta Ridge Regression Model (BRRM)

Abstract

Beta ridge regression is used to tackle the sensitivity of maximum likelihood estimation when regressors are linearly correlated in Beta generalized linear model. Cook’s distance is one of the renowned and classic tools for detection of outliers. In this article, we propose to use Cook’s distance with different residuals in the Beta ridge regression model. Simulated and real data are provided for illustration purposes. It has been observed that a class of weighted residuals performs better in outliers’ detection but there is no impact of small or large shrinkage parameter on detection.