Elimination of Variates in Linear Discrimination Problems

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Abstract
The selection of a subset from a larger list of variates measured on each individual in a study may be an important part of the analysis of medical research data. A number of statistical methods have been used in applications as rules for selecting subsets. The purpose of this study was to compare 3 of these selection rules with random selection. In order to study variate selection in linear discriminant applications to medical research, sets of data were obtained from 5 medical studies. Two of the rules selection of variates using stepwise regression, and selection using Studentized t differences in means, were observed to be better than random.

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