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Entropy 2015, 17(9), 6481-6502; doi:10.3390/e17096481

A Bayesian Predictive Discriminant Analysis with Screened Data

Department of Statistics, Dongguk University-Seoul, Pil-Dong 3Ga, Chung-Gu, Seoul 100-715, Korea
Academic Editors: Carlos De Bragança Pereira and Adriano Polpo
Received: 3 April 2015 / Revised: 25 August 2015 / Accepted: 17 September 2015 / Published: 21 September 2015
(This article belongs to the Special Issue Inductive Statistical Methods)
View Full-Text   |   Download PDF [438 KB, uploaded 21 September 2015]   |  

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
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Kim, H.-J. A Bayesian Predictive Discriminant Analysis with Screened Data. Entropy 2015, 17, 6481-6502.

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