Bayesian model selection in linear mixed effects models with autoregressive(p) errors using mixture priors

Please use this identifier to cite or link to this item: https://ir.lib.ncu.edu.tw/handle/987654321/105751

Title:	Bayesian model selection in linear mixed effects models with autoregressive(p) errors using mixture priors
Authors:	樊采虹;Fan, Tsai-Hung;Wang, Yi-Fu;Zhang, Yi-Chen
Contributors:	理學院統計研究所
Keywords:	Applied statistics;autoregressive models;Bayesian analysis;Computer simulation;Criteria;Error analysis;highest posterior probability model;linear mixed effects models;Mathematical models;median probability model;mixture priors;Monte Carlo methods;Probability distribution;Random errors;Random variables;Samples;Studies
Date:	2014-01-01
Issue Date:	2026-04-23 12:52:06 (UTC+8)
Publisher:	Routledge;Abingdon: Taylor & Francis
Abstract:	摘要： In this article, we apply the Bayesian approach to the linear mixed effect models with autoregressive(p) random errors under mixture priors obtained with the Markov chain Monte Carlo (MCMC) method. The mixture structure of a point mass and continuous distribution can help to select the variables in fixed and random effects models from the posterior sample generated using the MCMC method. Bayesian prediction of future observations is also one of the major concerns. To get the best model, we consider the commonly used highest posterior probability model and the median posterior probability model. As a result, both criteria tend to be needed to choose the best model from the entire simulation study. In terms of predictive accuracy, a real example confirms that the proposed method provides accurate results. 出版者： Abingdon: Taylor & Francis 出版日期： 2014-08-03 出處： Journal of applied statistics, 2014-08, Vol.41 (8), p.1814-1829 資源來源： EBSCOhost Business Source Premier 版權： 2014 Taylor & Francis 2014 版權： Copyright Taylor & Francis Ltd. 2014 識別號： ISSN: 0266-4763 識別號： EISSN: 1360-0532 識別號： DOI: 10.1080/02664763.2014.894001
Appears in Collections:	[Graduate Institute of Statistics] journal & Dissertation

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