Regression, Prediction and Shrinkage
研究了回归预测对新数据的拟合通常比原始数据差的现象，提出Stein型预测器以降低预测均方误差，并针对逐步回归的收缩问题给出预收缩预测器，适用于多元和逻辑回归模型。
Summary The fit of a regression predictor to new data is nearly always worse than its fit to the original data. Anticipating this shrinkage leads to Stein-type predictors which, under certain assumptions, give a uniformly lower prediction mean squared error than least squares. Shrinkage can be particularly marked when stepwise fitting is used: the shrinkage is then closer to that expected of the full regression rather than of the subset regression actually fitted. Preshrunk predictors for selected subsets are proposed and tested on a number of practical examples. Both multiple and binary (logistic) regression models are considered.