Implementing Restricted Maximum Likelihood Estimation in Structural Equation Models
Implementing Restricted Maximum Likelihood Estimation in Structural Equation Models
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DOI：
10.1080/10705511.2013.742404
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发表时间：
2013-01-01
影响因子：
6
通讯作者：
Cheung, Mike W. -L.
中科院分区：
文献类型：
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作者：
Cheung, Mike W. -L.
结构方程建模(SEM)现在是社会和行为科学中应用的许多多元技术的通用建模框架。许多统计模型既可以看作是结构方程的特例，也可以看作是潜变量模型框架的一部分。一种流行的扩展是使用结构方程进行线性混合效应建模(LMM)，例如横截面多水平建模和潜在增长建模。众所周知，LMM可以表示为结构方程模型。然而，SEM和LMM的实现之间的一个主要区别是，在SEM中通常使用最大似然(ML)估计，而在大多数LMM程序中，约束(或残差)最大似然(REML)估计是默认方法。本文展示了如何在结构模型中实现REML估计。文中以潜在增长模型和Meta分析为例，说明了在OpenMx中实现的步骤。讨论了在扫描电子显微镜中实施REML的相关问题。
Structural equation modeling (SEM) is now a generic modeling framework for many multivariate techniques applied in the social and behavioral sciences. Many statistical models can be considered either as special cases of SEM or as part of the latent variable modeling framework. One popular extension is the use of SEM to conduct linear mixed-effects modeling (LMM) such as cross-sectional multilevel modeling and latent growth modeling. It is well known that LMM can be formulated as structural equation models. However, one main difference between the implementations in SEM and LMM is that maximum likelihood (ML) estimation is usually used in SEM, whereas restricted (or residual) maximum likelihood (REML) estimation is the default method in most LMM packages. This article shows how REML estimation can be implemented in SEM. Two empirical examples on latent growth model and meta-analysis are used to illustrate the procedures implemented in OpenMx. Issues related to implementing REML in SEM are discussed.