Incorporating Marginal Prior Information in Latent Class Models
Incorporating Marginal Prior Information in Latent Class Models
复制标题
将边际先验信息纳入潜在类模型
DOI：
10.1214/15-ba959
复制
发表时间：
2016
影响因子：
4.4
通讯作者：
Jerome P. Reiter
中科院分区：
文献类型：
--
作者：
Tracy A. Schifeling;Jerome P. Reiter
。我们提出了一种方法，将关于边际概率的信息先验信念融入到分类数据的贝叶斯潜在类模型中。其基本思想是在原始数据上附加综合观察，以使(I)期望边际的经验分布与先前信念的经验分布相匹配，以及(Ii)其余变量的值被遗漏。先前不确定性的程度由增加的记录的数量控制。后验推断可以通过典型的潜在类模型的MCMC算法来获得，该算法被量身定做以科学地处理拼接数据中的缺失值(ffi)。我们使用基于美国社区调查数据的各种模拟来说明该方法，包括如何使用扩充记录来将潜在类别模型fi到来自分层样本的数据的例子。
. We present an approach to incorporating informative prior beliefs about marginal probabilities into Bayesian latent class models for categorical data. The basic idea is to append synthetic observations to the original data such that (i) the empirical distributions of the desired margins match those of the prior beliefs, and (ii) the values of the remaining variables are left missing. The degree of prior uncertainty is controlled by the number of augmented records. Posterior inferences can be obtained via typical MCMC algorithms for latent class models, tailored to deal efficiently with the missing values in the concatenated data. We illustrate the approach using a variety of simulations based on data from the American Community Survey, including an example of how augmented records can be used to fit latent class models to data from stratified samples.
使用样本调查权重的非参数贝叶斯建模。
影响因子：
0.8
作者：
Kunihama,T;Herring,AH;Halpern,CT;Dunson,DB
通讯作者：
Dunson,DB