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

被引：16

作者：

Lee, JC

Lin, TI

Lee, KJ

Hsu, YL

机构：

[1] Natl Chiao Tung Univ, Inst Stat, Hsinchu, Taiwan

[2] Natl Chiao Tung Univ, Grad Inst Finance, Hsinchu, Taiwan

[3] Tunghai Univ, Dept Stat, Taichung 40704, Taiwan

[4] Natl Chiao Tung Univ, Inst Stat, Hsinchu, Taiwan

[5] Natl Chung Hsing Univ, Dept Appl Math, Taichung 40227, Taiwan

来源：

JOURNAL OF STATISTICAL PLANNING AND INFERENCE | 2005年 / 133卷 / 02期

关键词：

approximate Bayesian; maximum likelihood estimation; MCMC; uniforni prior; random effects; reparameterization;

D O I：

10.1016/j.jspi.2004.03.015

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

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.

引用

页码：435 / 451

页数：17

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