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http://dx.doi.org/10.5351/CKSS.2009.16.4.675

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

Chung, Hie-Choon (Department of e-Business, Gwangju University)
Publication Information
Communications for Statistical Applications and Methods / v.16, no.4, 2009 , pp. 675-686 More about this Journal
Abstract
In this paper, 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. The classification rule depends on the situation when the training samples include missing values or not. We consider the bootstrap confidence intervals for classification error rate when a block of observation is missing.
Keywords
Bootstrap Confidence interval; error rate; block of missing observations; linear combination classification statistic; Jackknife method; Monte Carlo study;
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