A Bayesian Predictive Discriminant Analysis with Screened Data

Entropy

Hea-Jung Kim¹  Carlos De Bragan๺ Pereira¹ 

[1] Department of Statistics, Dongguk University-Seoul, Pil-Dong 3Ga, Chung-Gu, Seoul 100-715, Korea; E-Mail

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

DOI  :  10.3390/e17096481

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2015 by the author; licensee MDPI, Basel, Switzerland.