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 $\omega$ is considered. The efficiency properties of the new estimator are illustrated by a simulation study. Finally, the performance of the new estimator is evaluated for a real data set.

KEYWORDS:

• Difference-based estimator
• generalized Liu estimator
• generalized difference-based weighted mixed Liu estimator
• partially linear model
• weighted mixed estimator

AMS SUBJECT CLASSIFICATIONS:

• Primary: 62G08
• Secondary: 62J07

Acknowledgements

The authors are grateful to the anonymous reviewers, associate editor and editor-in-chief for their valuable comments helped to improve the quality of this work.

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCID

Mahdi Roozbeh http://orcid.org/0000-0001-8381-738X

Additional information

Funding

The third author's research is supported by Semnan University Research [grant number 266/97/17577].