An Adaptive Bayesian Lasso Approach with Spike-and-Slab Priors to Identify Multiple Linear and Nonlinear Effects in Structural Equation Models
An Adaptive Bayesian Lasso Approach with Spike-and-Slab Priors to Identify Multiple Linear and Nonlinear Effects in Structural Equation Models
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DOI：
10.1080/10705511.2018.1474114
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发表时间：
2018-01-01
影响因子：
6
通讯作者：
Kelava, Augustin
中科院分区：
文献类型：
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作者：
Brandt, Holger;Cambria, Jenna;Kelava, Augustin
在应用研究中，例如动机理论，通常许多变量在理论上是结果的隐含预测因子，并且假设了几种相互作用（例如，Watt，2004）。然而，估计问题，可能会出现时，同时分析几个相互作用和/或二次效应尚未调查，因为在结构方程建模框架中的相互作用效应的模拟研究主要集中在小模型，包含单一的相互作用效应。在这篇文章中，我们表明，传统的方法可以提供低精度的估计时，复杂的模型估计。我们引入了一个自适应贝叶斯套索方法与穗板先验，克服了这个问题。在模拟研究中使用复杂模型，我们表明，与标准贝叶斯套索方法和典型的频率主义方法（即，无约束产品指标法和潜在调节结构法）。
In applied research, such as with motivation theories, typically many variables are theoretically implied predictors of an outcome and several interactions are assumed (e.g., Watt, 2004). However, estimation problems that might arise when several interaction and/or quadratic effects are analyzed simultaneously have not been investigated because simulation studies on interaction effects in the structural equation modeling framework have mainly focused on small models that contain single interaction effects. In this article, we show that traditional approaches can provide estimates with low accuracy when complex models are estimated. We introduce an adaptive Bayesian lasso approach with spike-and-slab priors that overcomes this problem. Using a complex model in a simulation study, we show that the parameter estimates of the proposed approach are more accurate in situations with high multicollinearity or low reliability compared with a standard Bayesian lasso approach and typical frequentist approaches (i.e., unconstrained product indicator approach and latent moderated structures approach).