Bayesian dynamic modeling of latent trait distributions
Bayesian dynamic modeling of latent trait distributions
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DOI：
10.1093/biostatistics/kxj025
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发表时间：
2006-10-01
期刊：
影响因子：
2.1
通讯作者：
Dunson, David B.
中科院分区：
文献类型：
--
作者：
Dunson, David B.
对潜在特征的研究通常会收集衡量特征不同方面的多个项目的数据。对于这样的数据，通常考虑这样的模型，其中不同的项目是正态潜变量的表现，该正态潜变量通过线性回归模型依赖于协变量。本文提出了一种灵活的贝叶斯替代方法，其中未知的潜在变量密度可以在不同的预测器级别上动态地改变位置和形状。使用基本法线的比例混合，以便灵活地建模测量误差，并允许混合类别和连续比例。用Dirichlet过程的动态混合来表征潜伏期的分布。后验计算通过马尔可夫链蒙特卡罗算法进行，预测密度用作模型拟合的推断和评估的基础。这些方法是用一项研究氧化应激引起的DNA损伤的数据来说明的。
Studies of latent traits often collect data for multiple items measuring different aspects of the trait. For such data, it is common to consider models in which the different items are manifestations of a normal latent variable, which depends on covariates through a linear regression model. This article proposes a flexible Bayesian alternative in which the unknown latent variable density can change dynamically in location and shape across levels of a predictor. Scale mixtures of underlying normals are used in order to model flexibly the measurement errors and allow mixed categorical and continuous scales. A dynamic mixture of Dirichlet processes is used to characterize the latent response distributions. Posterior computation proceeds via a Markov chain Monte Carlo algorithm, with predictive densities used as a basis for inferences and evaluation of model fit. The methods are illustrated using data from a study of DNA damage in response to oxidative stress.