Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/37093
標題: Bayesian analysis of Box-Cox transformed linear mixed models with ARMA(p, q) dependence
作者: Lee, J.C.
林宗儀
Lin, T.I.
Lee, K.J.
Hsu, Y.L.
許英麟
關鍵字: approximate Bayesian
maximum likelihood estimation
MCMC
uniforni
prior
random effects
reparameterization
forecasting technological substitutions
longitudinal data
time
errors
inference
期刊/報告no:: Journal of Statistical Planning and Inference, Volume 133, Issue 2, Page(s) 435-451.
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://hdl.handle.net/11455/37093
ISSN: 0378-3758
文章連結: http://dx.doi.org/10.1016/j.jspi.2004.03.015
Appears in Collections:統計學研究所

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