ABSTRACT

Recent work on the Polya-Gamma distribution provides a breakthrough for the Bayesian modeling of logit, count, and nominal variables. We describe how the methodology is incorporated in the Mplus modeling framework and illustrate it with several examples: logistic latent growth models, multilevel IRT, multilevel time-series models for count data, multilevel nominal regression, and nominal factor analysis.

中文翻译：

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将贝叶斯结构方程、多级和混合模型扩展到 logit、负二项式和名义变量

摘要

最近关于 Polya-Gamma 分布的工作为 logit、计数和名义变量的贝叶斯建模提供了突破。我们描述了该方法是如何被纳入 Mplus 建模框架的，并通过几个示例对其进行说明：逻辑潜在增长模型、多级 IRT、计数数据的多级时间序列模型、多级名义回归和名义因子分析。