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- W325918808 abstract "Abstract In discriminant analysis, we consider a special pattern which contains a block ofmissing observations. We assume that the two populations are equally likely and thecosts of misclassi cation are equal. In this situation, we consider the bootstrap con -dence intervals of the error rate in the circular models when the covariance matricesare equal and not equal.Keywords: Block of missing observations, bootstrap con dence interval, circular model,error rate, linear combination classi cation statistic, Monte Carlo study. 1. Introduction In discriminant analysis the problem is to classify a px1 observation X of unknown originto one of several distinct populations using an appropriate classi cation rule. In this paper itwill be assumed that there are two distinct populations which are multivariate normal. Wealso assume that the two populations are equally likely and the costs of misclassi cation areequal. The classi cation rule depends on the situation when the training samples includemissing values or not. Assuming that the covariance matrices are circular, we make anappropriate transformation which reduces the circular matrices to canonical forms. Thediscriminant function is given when the populations are multivariate normal with di erentcircular matrices and the linear combination statistic is used when a block of observationsis missing. We consider the bootstrap con dence intervals of the error rate in the circularmodels when the covariance matrices are equal and not equal." @default.
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- W325918808 date "2009-01-01" @default.
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- W325918808 title "Bootstrap confidence intervals for classification error rate in circular models when a block of observations is missing" @default.
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