A new difference-based weighted mixed Liu estimator in partially linear models

Abstract

In this paper, a generalized difference-based estimator is introduced for the vector parameter beta in the partially linear model when the errors are correlated. A generalized difference-based Liu estimator is defined for the vector parameter beta. Under the linear stochastic constraint r = R beta + e, a new generalized difference-based weighted mixed Liu estimator is introduced. The performance of this estimator over the generalized difference-based weighted mixed estimator and the generalized difference-based Liu estimator in terms of the mean squared error matrix criterion is investigated. Then, a method to select the biasing parameter d and non-stochastic weight. is considered. The efficiency properties of the newestimator are illustrated by a simulation study. Finally, the performance of the new estimator is evaluated for a real data set.