Snapinn S M, Knoke J D
Merck, Sharp & Dohme Research Laboratories, Brussels, Belgium.
Biometrics. 1989 Mar;45(1):289-99.
Accurate estimation of misclassification rates in discriminant analysis with selection of variables by, for example, a stepwise algorithm, is complicated by the large optimistic bias inherent in standard estimators such as those obtained by the resubstitution method. Application of a bootstrap adjustment can reduce the bias of the resubstitution method; however, the bootstrap technique requires the variable selection procedure to be repeated many times and is therefore difficult to compute. In this paper we propose a smoothed estimator that requires relatively little computation and which, on the basis of a Monte Carlo sampling study, is found to perform generally at least as well as the bootstrap method.
在判别分析中，例如通过逐步算法选择变量时，要准确估计误分类率会因标准估计器（如通过再代入法获得的估计器）中固有的巨大乐观偏差而变得复杂。应用自助法调整可以减少再代入法的偏差；然而，自助法技术要求变量选择过程重复多次，因此计算起来很困难。在本文中，我们提出了一种平滑估计器，它所需的计算相对较少，并且基于蒙特卡罗抽样研究发现，其总体表现通常至少与自助法一样好。
