Multivariate Behav Res. 2001 Oct 1;36(4):563-88. doi: 10.1207/S15327906MBR3604_04.
Inequality constraints among class specific probabilities can be used to assign a specific meaning to the classes in a latent class model. Different models arise if different sets of constraints are used. In this paper, model selection using Bayes factors, and, (pseudo) likelihood ratio statistics evaluated using posterior predictive p-values, will be discussed. It will be illustrated that these Bayesian selection criteria do not suffer from the same flaw as maximum likelihood based selection criteria. Using a small simulation study it will be shown that, in the context of the simulation study, Bayes factors and the pseudo likelihood ratio statistic have the best proporties. The article will be concluded with an example.
类别特定概率之间的不平等约束可用于为潜在类别模型中的类别赋予特定含义。如果使用不同的约束集，则会出现不同的模型。本文将讨论使用贝叶斯因子的模型选择，以及使用后验预测 p 值评估的（伪）似然比统计量。将说明这些贝叶斯选择标准不会像最大似然选择标准那样存在缺陷。通过一个小型模拟研究，将表明在模拟研究的背景下，贝叶斯因子和伪似然比统计量具有最佳特性。文章将以一个示例结束。
