Conditional Bootstrap Confidence Intervals for Classification Error Rate When a Block of Observations Is Missing

• Activity Number: 38
• Type: Contributed
• Date/Time: Sunday, August 1, 2010 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistical Computing
• Author(s): Hie-Choon Chung*+ and Chien-Pai Han
• Companies: Gwangju University and The University of Texas at Arlington
• Keywords: Monte Carlo study ; conditional bootstrap confidence intervals ; error rate ; linear combination classification statistic

Abstract

In this research, it will be assumed that there are two distinct populations which are multivariate normal with equal covariance matrix. We also assume that the two populations are equally likely and the costs of misclassification are equal. We propose conditional bootstrap confidence intervals when the training samples contain a block of missing observations. In this situation, the conditional bootstrap confidence intervals are evaluated for the error rate using linear combination classification statistic. This confidence intervals are compared with the one which is constructed by the unconditional bootstrap method. A Monte Carlo study is conducted to evaluate the coverage probability and the average length of the confidence interval.