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TitleTwo-level Mean and Covariance Structures: Maximum Likelihood via an EM Algorithm
Creator
Date Issued2004-01
Source PublicationMultilevel Modeling Methodological Advances, Issues, and Applications
ISBN9780805851700
Author/Editor of Source PublicationSteven P. Reise, Naihua Duan
Book seriesMultivariate Applications Series
Publication PlaceUSA
PublisherPsychology Press
Abstract

An EM-type gradient algorithm for analysis of maximum likelihood estimation of the two-level structural equation model with both mean and
covariance structures is proposed. The model considered in this paper is
a generalization of that studied by Lee and Poon (1998). Approximate
standard error of the maximum likelihood estimation and the chi-squared
statistic for testing the model fit are given. Simulation studies show that
the proposed EM gradient algorithm converges very fast and need not be
accelerated by existing techniques in the literature

Language英语English
KeywordEM gradient algorithm maximum likelihood estimation mean and covariance structures multilevel structural equation modeling multivariate normal distribution
Citation statistics
Document TypeBook chapter
Identifierhttp://repository.uic.edu.cn/handle/39GCC9TT/5186
CollectionResearch outside affiliated institution
Affiliation
University of California, Los Angeles Department of Psychology, Box 951563 Los Angeles, CA 90095-1563, U.S.A.
Recommended Citation
GB/T 7714
Peter M. Bentler,Jiajuan Liang. Two-level Mean and Covariance Structures: Maximum Likelihood via an EM Algorithm. USA: Psychology Press, 2004.
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