A Comparison of the Stein-Rule and Positive-Part Stein-Rule Estimators in a Misspecified Linear Regression Model
研究了当模型遗漏相关变量时，Stein规则和正部分Stein规则估计量的预测风险，推导了后者的精确公式，并给出其优于前者的充分条件。数值计算表明，即使存在遗漏变量，正部分Stein规则估计量也是OLS、SR和PSR中的最佳选择。
In this paper, we examine the performance of the predictive risk of the Steinrule (SR) and positive-part Stein-rule (PSR) estimators when relevant regressors are omitted in the specified model. The exact formula of the predictive risk of the PSR estimator is derived, and the sufficient condition for the PSR estimator to dominate the SR estimator under a specification error is given. It is shown by numerical computation that the PSR estimator seems to be the best choice among the OLS, SR, and PSR estimators even when there are omitted variables.