Abstract #300892


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JSM 2002 Abstract #300892
Activity Number: 48
Type: Contributed
Date/Time: Sunday, August 11, 2002 : 4:00 PM to 5:50 PM
Sponsor: Business & Economics Statistics Section*
Abstract - #300892
Title: Estimation of Error Rate for the Linear Discriminant Function under the High-Dimensionality with Small-Sample Setting
Author(s): Peter Chen*+ and Dean Young
Affiliation(s): University of Mary Hardin-Baylor and Baylor University
Address: P.O. Box 8420, UMHB Station, Belton, Texas, 76513, USA
Keywords: linear discriminant function ; asymptotic approximation ; Monte Carlo simulation ; conditional error rate ; Mahalanobis distance
Abstract:

In this paper, we propose a new estimator of the conditional error rate (CER) for the linear discriminant function, which performs favorably to other currently-available methods under the high-dimensionality and small-sample condition. Also, we compare various CER estimators implemented by two different estimators for the Mahalanobis distance. We find that Dorans shrunken-generalized-distance expression improves the efficacy of some CER estimators when pattern classes are close together.


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