2 research outputs found
Superiority of the Stochastic Restricted Liu Estimator under misspecification
No full textThis paper deals with the use of correct prior infromation in the estimation of regression coefficients when the regression model is misspecified due to the exclusion of some relevant regressor variables. In particular, the attention is focused on the Stochastic Restricted Liu estimator introduced by Hubert and Wijekoon (2004), which outperforms Liu estimator with respect to the matrix mean squared error matrix criterion. Further the superiority of the Stochastic Restricted Liu predictor over the Liu predictor is also examined, and concluded that there are situations where the Stochastic Restricted Liu predictor outperforms the Liu predictor with respect to the mean squared error matrix criterion even the model is misspecified
Mean square error matrix superiority of the mixed regression estimator under misspecification
No full textConditions are derived under which the mixed regression estimator (MRE) is better then the ordinary least-squares estimator (OLSE) with respect to the mean square error (MSE) matrix criterion especially for the case that the regression model is misspecified. Some attention is paid to prediction, where it is shown that the MRE-predictor is potentially superior to the OLS-predictor under the same criterion
