Journal of the Korean Statistical Society

The Korean Statistical Society (한국통계학회)
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A Predictive Two-Group Multinormal Classification Rule Accounting for Model Uncertainty

  • Kim, Hea-Jung (Department of Statistics, Dongguk University, Seoul 100-715)
  • Published : 1997.12.01
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Abstract

A new predictive classification rule for assigning future cases into one of two multivariate normal population (with unknown normal mixture model) is considered. The development involves calculation of posterior probability of each possible normal-mixture model via a default Bayesian test criterion, called intrinsic Bayes factor, and suggests predictive distribution for future cases to be classified that accounts for model uncertainty by weighting the effect of each model by its posterior probabiliy. In this paper, our interest is focused on constructing the classification rule that takes care of uncertainty about the types of covariance matrices (homogeneity/heterogeneity) involved in the model. For the constructed rule, a Monte Carlo simulation study demonstrates routine application and notes benefits over traditional predictive calssification rule by Geisser (1982).

Keywords

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