A New Biased Estimator in Linear Regression Model
Huibing Hao, Chunping Li
Available Online April 2016.
- https://doi.org/10.2991/emim-16.2016.67How to use a DOI?
- Lnear regression model; Stochastic restricted ridge estimator; Mixed estimator; Mean squared error matrix ; Biased estimator.
- 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Huibing Hao AU - Chunping Li PY - 2016/04 DA - 2016/04 TI - A New Biased Estimator in Linear Regression Model BT - 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 - https://doi.org/10.2991/emim-16.2016.67 ID - Hao2016/04 ER -