A new general biased estimator in linear measurement error model

Abstract

Numerous biased estimators are known to circumvent the multicollinearity problem in linear measurement error models. This article proposes a general biased estimator with the ridge regression and the Liu estimators as special cases. The efficiency of the suggested estimator is compared with ridge regression and Liu estimators under the mean squared error matrix criterion. In addition, a Monte Carlo simulation study and a numerical evaluation have been conducted to elucidate the superiority of the new general biased estimator over other estimators.

Keywords:

• Biased estimator
• multicollinearity
• measurement error
• mean squared error matrix
• Monte Carlo simulation

AMS Subject Classification::

• 62J07
• 62J05

Disclosure statement

No potential conflict of interest was reported by the author(s).

Acknowledgements

The authors wish to thank the editor and anonymous referees for valuable suggestions and comments which improved the quality of the presentation.