Variable selection and error rate estimation in discriminant analysis

We investigate the problems of variable selection and error rate estimation in discriminant analysis. A cross model validation based method that simultaneously addresses both these issues, is proposed. The performance of this method is compared to that of some procedures in the literature by means of a Monte Carlo simulation study. The actual error rate of the classification rule based on the selected variables and the probability of correct model selection are used to evaluate the selection performance of the procedures, and unconditional mean squared error is used as criterion to judge estimation accuracy. We find that the new proposal largely improves on existing procedures.