Stepwise Location Model Choice in Mixed-Variable Discrimination

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Abstract
One practical drawback to the use of discrimination methods based on the location model for mixtures of discrete and continuous variables is that the smoothing techniques employed, and the subsequent estimation of error rates, limit fairly severely the allowable number of discrete variables. A backward elimination method of discrete variable selection is outlined in this paper. This can be used to identify a suitable, reduced location model for discriminant applications when the number of discrete variables is too large for direct use. It can also be used more traditionally as a variable selection procedure in discriminant analysis. Some examples are given.

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