Finite Normal Mixture SEM Analysis by Fitting Multiple Conventional SEM Models.
Finite Normal Mixture SEM Analysis by Fitting Multiple Conventional SEM Models.
复制标题
DOI：
10.1111/j.1467-9531.2010.01224.x
复制
发表时间：
2010-08
影响因子：
3
通讯作者：
Bentler PM
中科院分区：
文献类型：
--
作者：
Yuan KH;Bentler PM
本文提出了一个两阶段的最大似然（ML）的方法来正常的混合结构方程模型（SEM），并发展统计推断，允许分布误指定。在第1阶段估计饱和均值和协方差以及一个双线性协方差矩阵。这些用于在第2阶段评估结构模型。在传统的SEM文献中积累的模型诊断和评估技术可以用来研究每个组件的模型结构。实例表明，两阶段ML方法导致正确或接近正确的模型，即使正常的混合假设被违反和初始模型被错误指定。与单阶段ML相比，两阶段ML避免了模型规格和组件数量的混淆效应，并且计算效率更高。蒙特-卡罗结果表明，在单级ML性能最好的情况下，两级ML仅损失最小的效率。蒙特-卡罗结果还表明，常用的模型选择准则BIC是更强大的分布违背饱和模型比结构模型在中等样本容量。所提出的两阶段ML方法在使用不同模型对不同组件进行建模方面也非常灵活。混合物建模文献中潜在的新发展可以很容易地适应正常混合物SEM的研究问题。
This paper proposes a two-stage maximum likelihood (ML) approach to normal mixture structural equation modeling (SEM), and develops statistical inference that allows distributional misspecification. Saturated means and covariances are estimated at stage-1 together with a sandwich-type covariance matrix. These are used to evaluate structural models at stage-2. Techniques accumulated in the conventional SEM literature for model diagnosis and evaluation can be used to study the model structure for each component. Examples show that the two-stage ML approach leads to correct or nearly correct models even when the normal mixture assumptions are violated and initial models are misspecified. Compared to single-stage ML, two-stage ML avoids the confounding effect of model specification and the number of components, and is computationally more efficient. Monte-Carlo results indicate that two-stage ML loses only minimal efficiency under the condition where single-stage ML performs best. Monte-Carlo results also indicate that the commonly used model selection criterion BIC is more robust to distribution violations for the saturated model than that for a structural model at moderate sample sizes. The proposed two-stage ML approach is also extremely flexible in modeling different components with different models. Potential new developments in the mixture modeling literature can be easily adapted to study issues with normal mixture SEM.
登录
查看更多内容
DOI：
10.1080/10705519909540118
发表时间：
1999-01-01
影响因子：
6
作者：
Hu, Li-tze;Bentler, Peter M.
通讯作者：
Bentler, Peter M.
影响因子：
3
作者：
BOOMSMA, A
通讯作者：
BOOMSMA, A
影响因子：
3
作者：
JORESKOG, KG
通讯作者：
JORESKOG, KG
DOI：
10.1109/34.865189
发表时间：
2000-07-01
影响因子：
23.6
作者：
Biernacki, C;Celeux, G;Govaert, G
通讯作者：
Govaert, G
DOI：
10.1207/s15328007sem0703_2
发表时间：
2000-01-01
影响因子：
6
作者：
Fouladi, Rachel T.
通讯作者：
Fouladi, Rachel T.