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- 저자 : Karunamuni Rohana J. (검색결과 1 건)
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An empirical classification procedure for nonparametric mixture models
Zhao Qiang,Karunamuni Rohana J.,Wu Jingjing 한국통계학회 2020 Journal of the Korean Statistical Society Vol.49 No.3
Suppose that there are two populations which are mixed in proportions λ and (1−λ), respectively, and an investigator wishes to classify an individual into one of these two populations based on a p-dimensional observation on the individual. This is the basic classification problem with applications in wide variety of fields. In practice, the optimal rule (Bayes rule) is not available and thus need to be estimated when either the densities of the populations or the mixing proportion λ are not completely specified. This paper presents a nonparametric classification procedure based on kernel estimates for the most general case that both the densities and the mixing proportion are unknown. The error rate of the proposed procedure is calculated and compared with that of the optimal rule. Convergence rate of the difference in error rate are also established. A Monte Carlo simulation study and a real data example are given to compare the proposed rule with the optimal rule for a variety of cases.
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