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

Jack C. Lee; Tsung I. Lin; Kuo J. Lee; Ying L. Hsu

doi:10.1016/j.jspi.2004.03.015

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

Jack C. Lee, Tsung I. Lin, Kuo J. Lee, Ying L. Hsu

Department of Statistics

Research output: Contribution to journal › Article › peer-review

15 Citations (Scopus)

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.

Original language	English
Pages (from-to)	435-451
Number of pages	17
Journal	Journal of Statistical Planning and Inference
Volume	133
Issue number	2
DOIs	https://doi.org/10.1016/j.jspi.2004.03.015
Publication status	Published - 2005 Aug 1

All Science Journal Classification (ASJC) codes

Statistics and Probability
Statistics, Probability and Uncertainty
Applied Mathematics

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10.1016/j.jspi.2004.03.015

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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.",

author = "Lee, {Jack C.} and Lin, {Tsung I.} and Lee, {Kuo J.} and Hsu, {Ying L.}",

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N2 - 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.

AB - 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.

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

Abstract

All Science Journal Classification (ASJC) codes

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