A new stochastic restricted two-parameter estimator in multiple linear regression model

Sivarasa Arumairajan and Sinnarasa Kayathiri

Abstract

In this paper, we proposed a biased estimator, a new stochastic restricted two-parameter estimator (NSRTPE), for the multiple linear regression model to tackle the multicollinearity problem when the stochastic restrictions are available. Necessary and sufficient conditions for the superiority of the proposed estimator over the ordinary least square estimator (OLSE), ridge estimator (RE), Liu estimator (LE), almost unbiased Liu estimator (AULE), modified new two-parameter estimator (MNTPE), mixed estimator (ME), stochastic restricted Liu estimator (SRLE) were derived in the mean square error matrix (MSEM) criterion. Finally, we showed the superiority of the estimator proposed using a simulation study and a real-world example in the scalar mean square error (SMSE) criterion.

Keywords: Multiple linear regression, Multicollinearity, Stochastic restriction, Two-parameter estimator, Mean square error matrix

Full text:

Cite as: Arumairajan, S. and Kayathiri, S., 2022. A new stochastic restricted two-parameter estimator in multiple linear regression model. Vavuniya Journal of Science, 1(1):38-47.