Bayesian analysis of Box-Cox transformed linear mixed models with ARMA(p, q) dependence

doi:10.1016/j.jspi.2004.03.015

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DC 欄位	值	語言
dc.contributor.author	Lee, JC	en_US
dc.contributor.author	Lin, TI	en_US
dc.contributor.author	Lee, KJ	en_US
dc.contributor.author	Hsu, YL	en_US
dc.date.accessioned	2014-12-08T15:18:42Z	-
dc.date.available	2014-12-08T15:18:42Z	-
dc.date.issued	2005-08-01	en_US
dc.identifier.issn	0378-3758	en_US
dc.identifier.uri	http://dx.doi.org/10.1016/j.jspi.2004.03.015	en_US
dc.identifier.uri	http://hdl.handle.net/11536/13451	-
dc.description.abstract	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.	en_US
dc.language.iso	en_US	en_US
dc.subject	approximate Bayesian	en_US
dc.subject	maximum likelihood estimation	en_US
dc.subject	MCMC	en_US
dc.subject	uniforni prior	en_US
dc.subject	random effects	en_US
dc.subject	reparameterization	en_US
dc.title	Bayesian analysis of Box-Cox transformed linear mixed models with ARMA(p, q) dependence	en_US
dc.type	Article	en_US
dc.identifier.doi	10.1016/j.jspi.2004.03.015	en_US
dc.identifier.journal	JOURNAL OF STATISTICAL PLANNING AND INFERENCE	en_US
dc.citation.volume	133	en_US
dc.citation.issue	2	en_US
dc.citation.spage	435	en_US
dc.citation.epage	451	en_US
dc.contributor.department	統計學研究所	zh_TW
dc.contributor.department	資訊管理與財務金融系註：原資管所+財金所	zh_TW
dc.contributor.department	Institute of Statistics	en_US
dc.contributor.department	Department of Information Management and Finance	en_US
dc.identifier.wosnumber	WOS:000229660800013	-
dc.citation.woscount	11	-
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