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segmented classification analysis with a class of rectangle-screened elliptical populations

JOURNAL OF APPLIED STATISTICS 2015年第9期42卷 1877-1895页

作者： Kim, Hea-Jung Dongguk Univ Seoul Dept Stat Seoul 100715 South Korea

In many practical situations, a statistical practitioner often faces a problem of classifying an object from one of the segmented (or screened) populations where the segmentation was conducted by a set of screening variables. This paper addresses this problem, proposing and studying yet another optimal rule for classification with segmented populations. A class of q-dimensional rectangle-screened elliptically contoured (RSEC) distributions is considered for flexibly modeling the segmented populations. Based on the properties of the RSEC distributions, a parametric procedure for the segmented classification analysis (SCA) is proposed. This includes motivation for the SCA as well as some theoretical propositions regarding its optimal rule and properties. These properties allow us to establish other important results which include an efficient estimation of the rule by the Monte Carlo expectation-conditional maximization algorithm and an optimal variable selection procedure. Two numerical examples making use of utilizing a simulation study and a real dataset application and advocating the SCA procedure are also provided.

关键词： segmented classification rectangle-screened elliptically contoured distribution optimalrule variable selection MCECM algorithm 62E10 62F30

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Best Linear classification Rule for Multivariate Interval Screened Data

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COMMUNICATIONS IN STATISTICS-THEORY AND METHODS 2015年第8期44卷 1669-1690页

作者： Kim, Hea-Jung Dongguk Univ Seoul Dept Stat Seoul 100715 South Korea

In classification analysis, the target variable is often in practice defined by an underlying multivariate interval screening scheme. This engenders the problem of properly characterizing the screened populations as well as that of obtaining a classification procedure. Such problems paved the way for the development of yet another linear classification procedure and the incorporation of a class of skew-elliptical distributions for describing evolutions in the populations. To render the linear procedure effective, this article considers derivation and properties of the classification procedure as well as efficient estimation. The procedure is illustrated in applications to real and simulation data.

关键词： Bayes procedure Best linear procedure Multivariate interval screened data segmented classification Skew scale mixture of normal distribution

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