Adjusted ridge estimator and comparison with Kibria’s method in linear regression

Abstract

This paper proposes an adjusted ridge regression estimator for β for the linear regression model. The merit of the proposed estimator is that it does not require estimating the ridge parameter k unlike other existing estimators. We compared our estimator with an ordinary least squares (LS) estimator and with some well known estimators proposed by Hoerl and Kennard (1970), ordinary ridge regression (RR) estimator and generalized ridge regression (GR) and some estimators proposed by Kibria (2003) among others. A simulation study has been conducted and compared for the performance of the estimators in the sense of smaller mean square error (MSE). It appears that the proposed estimator is promising and can be recommended to the practitioners.