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A SEMIPARAMETRIC BAYESIAN APPROACH TO MULTIVARIATE LONGITUDINAL DATA
Australian & New Zealand Journal of Statistics ( IF 1.1 ) Pub Date : 2010-09-14 , DOI: 10.1111/j.1467-842x.2010.00581.x
Pulak Ghosh 1 , Timothy Hanson
Affiliation  

We extend the standard multivariate mixed model by incorporating a smooth time effect and relaxing distributional assumptions. We propose a semiparametric Bayesian approach to multivariate longitudinal data using a mixture of Polya trees prior distribution. Usually, the distribution of random effects in a longitudinal data model is assumed to be Gaussian. However, the normality assumption may be suspect, particularly if the estimated longitudinal trajectory parameters exhibit multimodality and skewness. In this paper we propose a mixture of Polya trees prior density to address the limitations of the parametric random effects distribution. We illustrate the methodology by analyzing data from a recent HIV-AIDS study.

中文翻译:

多变量纵向数据的半参数贝叶斯方法

我们通过合并平滑时间效应和放宽分布假设来扩展标准多元混合模型。我们提出了一种使用 Polya 树先验分布混合的多变量纵向数据的半参数贝叶斯方法。通常,纵向数据模型中随机效应的分布被假定为高斯分布。然而,正态性假设可能是可疑的,特别是如果估计的纵向轨迹参数表现出多峰性和偏度。在本文中,我们提出了 Polya 树先验密度的混合,以解决参数随机效应分布的局限性。我们通过分析最近一项 HIV-AIDS 研究的数据来说明该方法。
更新日期:2010-09-14
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