Ghosh Joyee, Herring Amy H, Siega-Riz Anna Maria
Department of Statistics and Actuarial Science, The University of Iowa, Iowa City, Iowa 52242, USA.
Biometrics. 2011 Sep;67(3):917-25. doi: 10.1111/j.1541-0420.2010.01502.x. Epub 2010 Oct 29.
In this article, we develop a latent class model with class probabilities that depend on subject-specific covariates. One of our major goals is to identify important predictors of latent classes. We consider methodology that allows estimation of latent classes while allowing for variable selection uncertainty. We propose a Bayesian variable selection approach and implement a stochastic search Gibbs sampler for posterior computation to obtain model-averaged estimates of quantities of interest such as marginal inclusion probabilities of predictors. Our methods are illustrated through simulation studies and application to data on weight gain during pregnancy, where it is of interest to identify important predictors of latent weight gain classes.
在本文中，我们开发了一种潜在类别模型，其类别概率取决于个体特定的协变量。我们的一个主要目标是识别潜在类别的重要预测因子。我们考虑的方法能够在允许变量选择不确定性的同时估计潜在类别。我们提出了一种贝叶斯变量选择方法，并实现了一种随机搜索吉布斯采样器用于后验计算，以获得感兴趣量的模型平均估计值，如预测因子的边际包含概率。我们通过模拟研究以及将其应用于孕期体重增加的数据来说明我们的方法，在该数据中识别潜在体重增加类别的重要预测因子是有意义的。
