Bayesian Subset Selection for Reproductive Dispersion Linear Models

Zhao Yuanying (), Xu Dengke, Duan Xingde and Pang Yicheng
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Zhao Yuanying: College of Mathematics and Information Science, Guiyang University, Guiyang, 550005, China
Xu Dengke: Department of Statistics, Zhejiang Agriculture and Forest University, Lin’an, 311300, China
Duan Xingde: Department of Mathematics, Chuxiong Normal College, Chuxiong, 675000, China
Pang Yicheng: School of Mathematics and Statistics, Guizhou University of Finance and Economics, Guiyang, 550025, China

Journal of Systems Science and Information, 2014, vol. 2, issue 1, 77-85

Abstract: We propose a full Bayesian subset selection method for reproductive dispersion linear models, which bases on expanding the usual link function to a function that incorporates all possible subsets of predictors by adding indictors as parameters. The vector of indicator variables dictates which predictors to delete. An efficient MCMC procedure that combining Gibbs sampler and Metropolis-Hastings algorithm is presented to approximate the posterior distribution of the indicator variables. The promising subsets of predictors can be identified as those with higher posterior probability. Several numerical examples are used to illustrate the newly developed methodology.

Keywords: Bayesian subset selection; Gibbs sampler; Metropolis-Hastings algorithm; reproductive dispersion linear models (search for similar items in EconPapers)
Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:bpj:jossai:v:2:y:2014:i:1:p:77-85:n:7

DOI: 10.1515/JSSI-2014-0077

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