Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
1155243 | Statistics & Probability Letters | 2008 | 9 Pages |
Abstract
This paper deals with the estimation problem in a system of two seemingly unrelated regression equations where the regression parameter is distributed according to the normal prior distribution N(β0,Ïβ2Σβ). Resorting to the covariance adjustment technique, we obtain the best Bayes estimator of the regression parameter and prove its superiority over the best linear unbiased estimator (BLUE) in terms of the mean square error (MSE) criterion. Also, under the MSE criterion, we show that the empirical Bayes estimator of the regression parameter is better than the Zellner type estimator when the covariance matrix of error variables is unknown.
Keywords
Related Topics
Physical Sciences and Engineering
Mathematics
Statistics and Probability
Authors
Lichun Wang, Noël Veraverbeke,