Bayesian methods for analyzing structural equation models with covariates, interaction, and quadratic latent variables
Bayesian methods for analyzing structural equation models with covariates, interaction, and quadratic latent variables
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用于分析具有协变量、交互作用和二次潜变量的结构方程模型的贝叶斯方法
DOI：
10.1080/10705510701301511
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发表时间：
2007-01-01
影响因子：
6
通讯作者：
Tang, Nian-Sheng
中科院分区：
文献类型：
--
作者：
Lee, Sik-Yum;Song, Xin-Yuan;Tang, Nian-Sheng
潜变量之间的相互作用分析受到了广泛的关注。本文介绍了一种贝叶斯方法来分析一个一般的结构方程模型，其中包含了潜变量和协变量的一般非线性项。这种方法产生的贝叶斯估计，具有相同的统计最佳属性作为最大似然估计。与传统方法相比，其他优点进行了讨论。更重要的是，我们通过例子演示了如何使用免费的软件WinBUGS获得贝叶斯估计和模型比较的结果。模拟研究进行评估的经验表现的方法的情况下，不同的样本大小和先前的输入。
The analysis of interaction among latent variables has received much attention. This article introduces a Bayesian approach to analyze a general structural equation model that accommodates the general nonlinear terms of latent variables and covariates. This approach produces a Bayesian estimate that has the same statistical optimal properties as a maximum likelihood estimate. Other advantages over the traditional approaches are discussed. More important, we demonstrate through examples how to use the freely available software WinBUGS to obtain Bayesian results for estimation and model comparison. Simulation studies are conducted to assess the empirical performances of the approach for situations with various sample sizes and prior inputs.