JSM 2004 - Toronto

Abstract #301884

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Activity Number: 415
Type: Contributed
Date/Time: Thursday, August 12, 2004 : 8:30 AM to 10:20 AM
Sponsor: Biometrics Section
Abstract - #301884
Title: Estimating Misclassification Error with Small Samples
Author(s): Wenjiang J. Fu*+ and Raymond J. Carroll
Companies: Texas A&M University and Texas A&M University
Address: 447 Blocker Bldg., College Station, TX, 77843,
Keywords: bioinformatics ; bootstrap ; cross-validation ; microarray data ; misclassification error ; small samples
Abstract:

One important issue in bioinformatics is the estimation of misclassification error. Although a variety of methods have been proposed, such as cross-validation (CV), resubstitution, and bootstrap, estimation of misclassification error still remains a major challenge in small sample studies, especially in microarray studies. It is known that CV is unbiased but highly variable while resubstitution is biased. We thus propose a resampling approach--the bootstrap cross-validation (BCV) method--and assess its performance through simulations in comparison with other methods, i.e., the CV and resubstitution. The simulation results show that BCV performs consistently better than CV and the resubstitution. We will also apply the method to a microarray study to provide a comparison in real data analysis.

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Revised March 2004