Bayesian analysis of a linear mixed model with AR(p) errors via MCMC

被引：1
|
作者：
Alkhamisi, MA [1 ]
Shukur, G
机构：
[1] Salahaddin Univ, Dept Math, Kurdistan Region, Iraq
[2] Vaxjo Univ, Dept Econ & Stat, Vaxjo, Sweden
来源：
JOURNAL OF APPLIED STATISTICS | 2005年 / 32卷 / 07期
关键词：
linear mixed model; autoregressive process; Metropolis-Hastings algorithm; Gibbs sampling; Bayesian statistics; autocorrelation; repeated measurement designs;
D O I：
10.1080/02664760500079688
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：
We develop Bayesian procedures to make inference about parameters of a statistical design with autocorrelated error terms. Modelling treatment effects can be complex in the presence of other factors such as time; for example in longitudinal data. In this paper, Markov chain Monte Carlo methods (MCMC), the Metropolis - Hastings algorithm and Gibbs sampler are used to facilitate the Bayesian analysis of real life data when the error structure can be expressed as an autoregressive model of order p. We illustrate our analysis with real data.
引用
收藏
页码：741 / 755
页数：15
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