Journal of the Korean Data and Information Science Society

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Conditional bootstrap confidence intervals for classification error rate when a block of observations is missing

  • Chung, Hie-Choon (Department of Healthcare Management, Gwangju University) ;
  • Han, Chien-Pai (Department of Mathematics, University of Texas at Arlington)
  • Received : 2012.11.29
  • Accepted : 2013.01.17
  • Published : 2013.01.31
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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 whether the training samples include missing values or not. We consider the conditional bootstrap confidence intervals for classification error rate when a block of observation is missing.

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References

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