标题： 预测样本外的收缩估计量：条件性能 显示英文标题

标题： Shrinkage estimators for prediction out-of-sample: Conditional performance

摘要： 我们发现，在线性模型中，James-Stein估计量尽管在样本内预测误差方面优于最大似然估计量，但在样本外预测方面却可能表现得比最大似然估计量差。 我们对该现象进行了详细分析并讨论了其影响。 在评估估计量的预测性能时，我们将训练数据中的回归矩阵视为固定的，即条件于设计变量。 我们的研究结果与Baranchik（1973年，Ann. Stat. 1:312-321）以及最近Dicker（2012年，arXiv:1102.2952）在无条件性能评估中得出的结果形成对比。 显示英文摘要

摘要： We find that, in a linear model, the James-Stein estimator, which dominates the maximum-likelihood estimator in terms of its in-sample prediction error, can perform poorly compared to the maximum-likelihood estimator in out-of-sample prediction. We give a detailed analysis of this phenomenon and discuss its implications. When evaluating the predictive performance of estimators, we treat the regressor matrix in the training data as fixed, i.e., we condition on the design variables. Our findings contrast those obtained by Baranchik (1973, Ann. Stat. 1:312-321) and, more recently, by Dicker (2012, arXiv:1102.2952) in an unconditional performance evaluation.