Abstract #301041

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JSM 2003 Abstract #301041
Activity Number: 84
Type: Contributed
Date/Time: Monday, August 4, 2003 : 8:30 AM to 10:20 AM
Sponsor: Business & Economics Statistics Section
Abstract - #301041
Title: Maximum Posterior Probability Estimators of the Conditional Error Rate for the Linear Discriminate Function
Author(s): Peter H. Chen*+ and Dean M. Young
Companies: University of Mary Hardin-Baylor and Baylor University
Address: 4511 Oxford Dr., Temple, TX, 76502-7304,
Keywords: linear discriminant function ; posterior probability ; Monte Carlo simulation ; parametric bootstrap ; conditional probability of misclassification
Abstract:

We propose four estimators of the conditional error rate (CER) for the linear discriminant function based on the maximum posterior probability (MPP). The bias of two of our proposed MPP-based estimators is reduced via a parametric bootstrap. We compare the efficacy of these four new estimators with other currently available methods under a high-dimensionality to small-sample ratio condition by a Monte Carlo simulation. We find that our bias-corrected MPP-based estimators perform favorably compared to others in the study when pattern classes are close together.

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