A model comparison approach to posterior predictive model checks in Bayesian confirmatory factor analysis.
A model comparison approach to posterior predictive model checks in Bayesian confirmatory factor analysis.
复制标题
贝叶斯验证性因素分析中后验预测模型检查的模型比较方法。
DOI：
10.1080/10705511.2021.2012682
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Zhang, J.
中科院分区：
文献类型：
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作者：
Zhang, J.
后验预测模型检验（PPMC）是贝叶斯因子分析（BCFA）中常用的模型拟合评价方法。在标准PPMC程序中，通过比较基于ML的点估计值的位置与统计量的预测分布来量化模型失配。当点估计远离中心后验预测分布时，模型拟合较差。然而，这种方法不包括基于最大似然（ML）的点估计值的变异性。我们提出了一种新的PPMC方法的基础上比较后验预测分布的假设和饱和的BCFA模型。该方法使用饱和模型的预测分布作为参考，并使用Kolmogorov-Smirnov（KS）统计量来量化假设模型的局部失配。模拟研究的结果表明，饱和模型PPMC方法是一种准确的方法来确定局部模型失配，并可用于模型比较。本文还提供了一个真实的数据实例。
Posterior Predictive Model Checking (PPMC) is frequently used for model fit evaluation in Bayesian Confirmatory Factor Analysis (BCFA). In standard PPMC procedures, model misfit is quantified by comparing the location of an ML-based point estimate to the predictive distribution of a statistic. When the point estimate is far from the center posterior predictive distribution, model fit is poor. Not included in this approach, however, is the variability of the Maximum Likelihood (ML)-based point estimates. We propose a new method of PPMC based on comparing posterior predictive distributions of a hypothesized and saturated BCFA model. The method uses the predictive distribution of the saturated model as a reference and the Kolmogorov-Smirnov (KS) statistic to quantify the local misfit of hypothesized models. The results of the simulation study suggest that the saturated model PPMC approach was an accurate method of determining local model misfit and could be used for model comparison. A real data example is also provided in this study.