Fitting multivariage normal finite mixtures subject to structural equation modeling
Fitting multivariage normal finite mixtures subject to structural equation modeling
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DOI：
10.1007/bf02294853
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发表时间：
1998-09-01
期刊：
影响因子：
3
通讯作者：
Van der Maas, HLJ
中科院分区：
文献类型：
--
作者：
Dolan, CV;Van der Maas, HLJ
本文研究了结构方程模型下多元正态混合分布的拟合问题。一般模型包括公因子回归模型和结构回归模型。协方差和平均结构模型的引入减少了在拟合混合物时要估计的参数的数量，并使人们能够研究关于混合物中各成分之间差异的各种实质性假设。在一般模型中，各个参数可以受到等式、非线性和简单边界的约束。置信区间基于Hessian函数的倒数和似然分布。给出了几个插图和结果的仿真研究有关的置信区间的报告。
This paper is about fitting multivariate normal mixture distributions subject to structural equation modeling. The general model comprises common factor and structural regression models. The introduction of covariance and mean structure models reduces the number of parameters to be estimated in fitting the mixture and enables one to investigate a variety of substantive hypotheses concerning the differences between the components in the mixture. Within the general model, individual parameters can be subjected to equality, nonlinear and simple bounds constraints. Confidence intervals are based on the inverse of the Hessian and on the likelihood profile. Several illustrations are given and results of a simulation study concerning the confidence intervals are reported.