论文标题
Stan中有效的贝叶斯结构方程建模
Efficient Bayesian Structural Equation Modeling in Stan
论文作者
论文摘要
结构方程模型包括大量流行的统计模型，包括因子分析模型，某些混合模型及其扩展。由于我们通常具有多个相互依存的响应变量和多个潜在变量（也可以称为随机效应或隐藏变量），因此模型估计变得复杂，通常会导致缓慢且效率低下的MCMC样本。在本文中，我们描述并说明了Stan中贝叶斯SEM估计的一般，有效的方法，将其与R Package Blavaan中的先前实现形成鲜明对比（Merkle＆Rosseel，2018年）。在详细描述了方法之后，我们在多种情况下进行了实际比较。比较表明，新方法显然更好。我们还讨论了该方法可能扩展到心理学家感兴趣的其他模型的方法。
Structural equation models comprise a large class of popular statistical models, including factor analysis models, certain mixed models, and extensions thereof. Model estimation is complicated by the fact that we typically have multiple interdependent response variables and multiple latent variables (which may also be called random effects or hidden variables), often leading to slow and inefficient MCMC samples. In this paper, we describe and illustrate a general, efficient approach to Bayesian SEM estimation in Stan, contrasting it with previous implementations in R package blavaan (Merkle & Rosseel, 2018). After describing the approaches in detail, we conduct a practical comparison under multiple scenarios. The comparisons show that the new approach is clearly better. We also discuss ways that the approach may be extended to other models that are of interest to psychometricians.
