A Variable Selection Criterion in the Linear Programming Approaches to Discriminant Analysis*
针对线性规划判别分析缺乏变量选择准则的问题，基于刀切法提出一种新准则，用于区分显著与非显著变量，并通过实例展示其应用。
ABSTRACT There are numerous variable selection rules in classical discriminant analysis. These rules enable a researcher to distinguish significant variables from nonsignificant ones and thus provide a parsimonious classification model based solely on significant variables. Prominent among such rules are the forward and backward stepwise variable selection criteria employed in statistical software packages such as Statistical Package for the Social Sciences and BMDP Statistical Software. No such criterion currently exists for linear programming (LP) approaches to discriminant analysis. In this paper, a criterion is developed to distinguish significant from nonsignificant variables for use in LP models. This criterion is based on the “jackknife” methodology. Examples are presented to illustrate implementation of the proposed criterion.