标题:A non-iterative posterior sampling algorithm for Laplace linear regression model
作者:Yang, Fengkai; Yuan, Haijing
作者机构:[Yang, Fengkai; Yuan, Haijing] Shandong Univ, Sch Math, Jinan, Peoples R China.; [Yang, Fengkai; Yuan, Haijing] Shandong Univ, Sch Math & Stat, Weih 更多
通讯作者:Yuan, Haijing
通讯作者地址:[Yuan, HJ]Shandong Univ, Sch Math, Jinan, Peoples R China;[Yuan, HJ]Shandong Univ, Sch Math & Stat, Weihai, Peoples R China.
来源:COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION
出版年:2017
卷:46
期:3
页码:2488-2503
DOI:10.1080/03610918.2015.1056354
关键词:EM algorithm; Gibbs sampling; Inverse Bayes formulae; Laplace linear; regression; Sampling/important resampling
摘要:In this article, a non-iterative sampling algorithm is developed to obtain an independently and identically distributed samples approximately from the posterior distribution of parameters in Laplace linear regression model. By combining the inverse Bayes formulae, sampling/importance resampling, and expectation maximum algorithm, the algorithm eliminates the diagnosis of convergence in the iterative Gibbs sampling and the samples generated from it can be used for inferences immediately. Simulations are conducted to illustrate the robustness and effectiveness of the algorithm. Finally, real data are studied to show the usefulness of the proposed methodology.
收录类别:EI;SCOPUS;SCIE
WOS核心被引频次:1
Scopus被引频次:1
资源类型:期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85006124921&doi=10.1080%2f03610918.2015.1056354&partnerID=40&md5=81095acdbd3c99eacf46c4b844a0aa6e
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