A New Biased Estimator in Linear Regression Model

DOI

10.2991/emim-16.2016.67How to use a DOI?

Keywords

Lnear regression model; Stochastic restricted ridge estimator; Mixed estimator; Mean squared error matrix; Biased estimator.

Abstract

In this paper, we propose a new ridge type estimator to overcome the multicollinearity problem, and we call the new biased estimator as the stochastic restricted ridge estimator (SRRE). In the mean squared errors matrix sense SRRE will be compared with several other biased estimators. The necessary and sufficient conditions for the superiority of the new estimators SRRE over the the ridge estimator (RE) and the Mixed Regression Estimator (MRE) in the mean squared error matrix criterion are derived. A numerical example with Monte Carlo simulation is given to illustrate the theoretical results.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

TY  - CONF
AU  - Huibing Hao
AU  - Chunping Li
PY  - 2016/04
DA  - 2016/04
TI  - A New Biased Estimator in Linear Regression Model
BT  - Proceedings of the 6th International Conference on Electronic, Mechanical, Information and Management Society
PB  - Atlantis Press
SP  - 306
EP  - 310
SN  - 2352-538X
UR  - https://doi.org/10.2991/emim-16.2016.67
DO  - 10.2991/emim-16.2016.67
ID  - Hao2016/04
ER  -