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Partially Collapsed Gibbs Sampling for Linear Mixed-effects Models

机译:线性混合效应模型的部分折叠Gibbs采样

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This article presents a novel Bayesian analysis for linear mixed-effects models. The analysis is based on the method of partial collapsing that allows some components to be partially collapsed out of a model. The resulting partially collapsed Gibbs (PCG) sampler constructed to fit linear mixed-effects models is expected to exhibit much better convergence properties than the corresponding Gibbs sampler. In order to construct the PCG sampler without complicating component updates, we consider the reparameterization of model components by expressing a between-group variance in terms of a within-group variance in a linear mixed-effects model. The proposed method of partial collapsing with reparameterization is applied to the Merton's jump diffusion model as well as general linear mixed-effects models with proper prior distributions and illustrated using simulated data and longitudinal data on sleep deprivation.
机译:本文为线性混合效应模型提供了新颖的贝叶斯分析。该分析基于部分折叠的方法,该方法允许某些组件从模型中部分折叠。预期构造为适合线性混合效应模型的部分折叠的Gibbs(PCG)采样器将显示出比相应的Gibbs采样器更好的收敛性。为了构造PCG采样器而不使组件更新复杂化,我们考虑通过使用线性混合效应模型中的组内方差表示组间方差来考虑模型组件的重新参数化。所提出的带有重新参数化的部分崩溃方法被应用于Merton的跳跃扩散模型以及具有适当先验分布的一般线性混合效应模型,并使用关于睡眠剥夺的模拟数据和纵向数据进行了说明。

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