A New Stochastic Mized Liu Estimator in Linear Regression Model
This paper is concerned with the parameter estimation in linear regression model with additional stochastic linear restrictions. To overcome the multicollinearity problem, a new stochastic mixed Liu estimator is proposed and its efficiency is discussed. The new estimator is a generalization of the ordinary mixed estimator (OME) (Theil and Goldberger, 1961) and Liu estimator (LE) (Liu K, 1993). Necessary and sufficient conditions for the superiority of the new stochastic mixed Liu estimator over the OME, the Liu estimator, the estimator proposed by Hubert and Wijekoon (2006) and the estimator proposed by Hu Yang and Jianwen Xu (2007) in the mean squared error matrix (MSEM) sense are derived. Finally, a numerical example (Gruber, 1998) is given to illustrate some of the theoretical results.
Linear regression model Ordinary mized estimator Liu estimator Stochastic restricted Liu estimator Alternative stochastic restricted Liu estimator Mean square error matriz
Weibing Zuo Peng Cheng
College of Mathematics and Information Science, North China University of Water Conservancy and Hydroelectric, Zhengzhou, Henan, 450011, China
国际会议
The Third International Workshop on Applied Matriz Theory(第三届国际矩阵分析与应用会议)
杭州
英文
1487-1491
2009-07-09（万方平台首次上网日期，不代表论文的发表时间）