标题:Bayesian model selection based on parameter estimates from subsamples
作者:Zhang, J.;Jiang, W.;Shao, X.
作者机构:[Zhang, J] School of Mathematics, Shandong University, Jinan, 250100, China;[ Jiang, W] Department of Statistics, Northwestern University, Evanston, I 更多
通讯作者:Zhang, JS
通讯作者地址:[Zhang, JS]Shandong Univ, Sch Math, Jinan 250100, Peoples R China.
来源:Statistics & Probability Letters
出版年:2013
卷:83
期:4
页码:979-986
DOI:10.1016/j.spl.2012.12.020
关键词:Bayes factor;Consistency;Model selection;Schwarz\'s Bayesian information criterion (BIC);Self-normalization
摘要:We propose Bayesian model selection based on composite datasets, which can be constructed from various subsample estimates. The method remains consistent without fully specifying a probability model, and is useful for dependent data, when asymptotic variance of the parameter estimator is difficult to estimate.
收录类别:SCOPUS;SCIE
资源类型:期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-84873559659&doi=10.1016%2fj.spl.2012.12.020&partnerID=40&md5=e32f89a974849f696e057304da294037
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