Cross-Validation Shrinkage of Regression Predictors
研究了交叉验证中回归预测因子的收缩现象，将收缩斜率近似为拟合残差的可估函数，并发现残差异方差会影响最小二乘法的收缩程度，提出了一种非参数收缩乘子的新预测方法。
SUMMARY Copas (1983) suggests that shrinkage of predictors can be related to the least squares slope of actual on predicted values in a new set of data. Considering multiple regression in a crossvalidatory setting, this shrinkage slope is approximated by an estimable function of the fitted residuals. Patterns of heteroscedasticity of residual variance can lead to more, or less, shrinkage of least squares. A new predictor using a nonparametric shrinkage multiplier is proposed.