BIAS in apparent classification rates in stepwise discriminant analysis

Abstract

When p variables in a discriminant analysis are chosen by stepwise selection, the mean and percentage points of the apparent correct classification rates are positively biased as compared to the setting where p variables are not selected from a larger set. This bias due to subset selection is examined by Monte Carlo methods and compared to the bias due solely to resubstitution of the original sample. Both types of bias are intensified when the number of variables exceeds the degrees of freedom for error.

Keywords:

• subset selection
• error rates
• Monte Carlo