標題: Bayesian analysis of Box-Cox transformed linear mixed models with ARMA(p, q) dependence
作者: Lee, JC
Lin, TI
Lee, KJ
Hsu, YL
統計學研究所
資訊管理與財務金融系 註:原資管所+財金所
Institute of Statistics
Department of Information Management and Finance
關鍵字: approximate Bayesian;maximum likelihood estimation;MCMC;uniforni prior;random effects;reparameterization
公開日期: 1-八月-2005
摘要: In this paper, we present a Bayesian inference methodology for Box-Cox transformed linear mixed model with ARMA(p, q) errors using approximate Bayesian and Markov chain Monte Carlo methods. Two priors are proposed and put into comparisons in parameter estimation and prediction of future values. The advantages of Bayesian approach over maximum likelihood method are demonstrated by both real and simulated data. (c) 2004 Elsevier B.V. All rights reserved.
URI: http://dx.doi.org/10.1016/j.jspi.2004.03.015
http://hdl.handle.net/11536/13451
ISSN: 0378-3758
DOI: 10.1016/j.jspi.2004.03.015
期刊: JOURNAL OF STATISTICAL PLANNING AND INFERENCE
Volume: 133
Issue: 2
起始頁: 435
結束頁: 451
顯示於類別:期刊論文


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