Jackknifing K-L estimator in Generalized linear models

Abstract

It is a challenge in real application when modeling the relationship between the response variable and several explanatory variables when the existence of collinearity. Traditionally, in order to avoid this issue, several shrinkage estimators are proposed. Among them is the Kibria and Lukman estimator (K-L). In this study, a jackknifed version of K-L estimator is proposed in the generalized linear model that combines Jackknife procedure with K-L estimator to reduce the biasedness. Our Monte Carlo simulation results and the real data application related to inverse Gaussian regression model suggest that the proposed estimator can bring significant improvement relative to other competitor estimators, in terms of absolute bias and mean squared error