Bayesian semiparametric variable selection with applications to periodontal data.
Bayesian semiparametric variable selection with applications to periodontal data.
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DOI：
10.1002/sim.7255
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发表时间：
2017-06-30
影响因子：
2
通讯作者：
Bandyopadhyay D
中科院分区：
文献类型：
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作者：
Cai B;Bandyopadhyay D
对于使用线性混合模型的集群或纵向数据分析中的随机效应，通常采用正态假设。然而，这样的假设并不总是现实的，它可能导致估计的潜在偏差，特别是在考虑到变量选择的情况下。此外，非参数假设(如Dirichlet过程)在这些随机效应上的灵活性可能会导致中心问题，导致固定效应的解释和变量选择的困难。针对这些问题，我们提出了一种非参数随机效应模型中固定效应和随机效应选择的贝叶斯方法。我们通过中心潜变量对回归系数进行建模，这些潜变量以概率折断(PSB)尺度混合分布。通过使用中心潜变量的混合先验和协方差分解，我们可以避免上述问题，并允许从模型中有效地选择固定和随机效应。我们通过一个模拟的例子和一个牙周病研究的数据集的说明性应用，展示了我们所提出的方法相对于其他竞争方案的优势。
A normality assumption is typically adopted for the random effects in a clustered or longitudinal data analysis using a linear mixed model. However, such an assumption is not always realistic, and it may lead to potential biases of the estimates, especially when variable selection is taken into account. Furthermore, flexibility of nonparametric assumptions (e.g. Dirichlet process) on these random effects may potentially cause centering problems, leading to difficulty of interpretation of fixed effects and variable selection. Motivated by these problems, we proposed a Bayesian method for fixed and random effects selection in nonparametric random effects models. We modeled the regression coefficients via centered latent variables which are distributed as probit stick-breaking (PSB) scale mixtures. By using the mixture priors for centered latent variables along with covariance decomposition, we could avoid the aforementioned problems, and allow efficient selection of fixed and random effects from the model. We demonstrated the advantages of our proposed approach over other competing alternatives through a simulated example, and also via an illustrative application to a dataset from a periodontal disease study.