Abstract #300539

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JSM 2003 Abstract #300539
Activity Number: 419
Type: Contributed
Date/Time: Wednesday, August 6, 2003 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Computing
Abstract - #300539
Title: Bootstrap Confidence Intervals for Classification Error Rate when a Block of Observation is Missing
Author(s): Hie-Choon Chung*+ and Chien-Pai Han
Companies: Kwangju University and University of Texas
Address: Dept. of Industrial & Info. Engineering, Kwangju 503-703, , , Korea
Keywords: bootstrap confidence interval ; error rate ; block of missing observations ; linear combination classification statistic ; jackknife method ; Monte Carlo study
Abstract:

We consider bootstrap confidence intervals when the training samples contain a block of missing observations. In this situation, the bootstrap confidence intervals are obtained for the error rate using linear combination classification statistic. We also consider bootstrap confidence intervals for the optimal error rate when the data contain no missing observations. These confidence intervals are compared with the one which is constructed by the jackknife method. A Monte Carlo study is conducted to evaluate the coverage probability and the average length of the confidence interval. A numerical example is given to illustrate its use.

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