A penalized likelihood method for multi-group structural equationmodelling
A penalized likelihood method for multi-group structural equationmodelling
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DOI：
10.1111/bmsp.12130
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发表时间：
2018-11-01
影响因子：
2.6
通讯作者：
Huang, Po-Hsien
中科院分区：
文献类型：
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作者：
Huang, Po-Hsien
在过去的二十年中，具有稀疏性的统计建模已经成为统计学和机器学习领域的一个活跃的研究课题。最近，Huang， Chen和Weng （2017, Psychometrika, 82, 329）和Jacobucci， Grimm, and McArdle （2016, structure Equation Modeling: A Multidisciplinary Journal, 23, 555）都提出了用于结构方程建模（SEM）的稀疏估计方法。然而，这些方法仅限于进行单组分析。本工作的目的是建立多组扫描电镜的惩罚似然（PL）方法。我们提出的方法将每个组模型参数分解为一个公共引用组件和一个特定于组的增量组件。通过惩罚增量分量，可以探索整个总体参数值的异质性，因为预期零组特定效应将减少。我们开发了一种期望条件最大化算法来优化PL标准。通过数值实验和实际数据算例验证了该方法的潜在实用性。
In the past two decades, statistical modelling with sparsity has become an active research topic in the fields of statistics and machine learning. Recently, Huang, Chen and Weng (2017, Psychometrika, 82, 329) and Jacobucci, Grimm, and McArdle (2016, Structural Equation Modeling: A Multidisciplinary Journal, 23, 555) both proposed sparse estimation methods for structural equationmodelling (SEM). These methods, however, are restricted to performing single-group analysis. The aim of the present work is to establish a penalized likelihood (PL) method for multi-group SEM. Our proposed method decomposes each group model parameter into a common reference component and a group-specific increment component. By penalizing the increment components, the heterogeneity of parameter values across the population can be explored since the null group-specific effects are expected to diminish. We developed an expectation-conditional maximization algorithm to optimize the PL criteria. A numerical experiment and a real data example are presented to demonstrate the potential utility of the proposed method.