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Department of Statistics, Dongguk University-Seoul, Pil-Dong 3Ga, Chung-Gu, Seoul 100-715, Korea





Academic Editors: Carlos De Bragança Pereira and Adriano Polpo

Abstract In the application of discriminant analysis, a situation sometimes arises where individual measurements are screened by a multidimensional screening scheme. For this situation, a discriminant analysis with screened populations is considered from a Bayesian viewpoint, and an optimal predictive rule for the analysis is proposed. In order to establish a flexible method to incorporate the prior information of the screening mechanism, we propose a hierarchical screened scale mixture of normal HSSMN model, which makes provision for flexible modeling of the screened observations. An Markov chain Monte Carlo MCMC method using the Gibbs sampler and the Metropolis–Hastings algorithm within the Gibbs sampler is used to perform a Bayesian inference on the HSSMN models and to approximate the optimal predictive rule. A simulation study is given to demonstrate the performance of the proposed predictive discrimination procedure. View Full-Text

Keywords: Bayesian predictive discriminant analysis; hierarchical model; MCMC method; optimal rule; scale mixture; screened observation Bayesian predictive discriminant analysis; hierarchical model; MCMC method; optimal rule; scale mixture; screened observation





Autor: Hea-Jung Kim

Fuente: http://mdpi.com/



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