title: Estimation of error rates in discriminant analysis with selection of variables.
abstract: 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.
authors.affiliation: Merck, Sharp & Dohme Research Laboratories, Brussels, Belgium.
authors.forename: S M
authors.initials: SM
authors.lastname: Snapinn
authors.forename: J D
authors.initials: JD
authors.lastname: Knoke
mesh_terms: D000465:Algorithms; D001699:Biometry; D006333:Heart Failure / Q000378:metabolism / Q000401:mortality; D006801:Humans; D008954:Models, Biological; D015233:Models, Statistical; D009010:Monte Carlo Method; D012044:Regression Analysis; D012494:Sampling Studies