Location of Repository

Two-level Mean and Covariance Structures: Maximum Likelihood via an EM Algorithm

By Peter M. Bentler and Jiajuan Liang

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. Key Words: EM gradient algorithm, maximum likelihood estimation, mean and covariance structures, multilevel structural equation modeling, multivariate normal distribution # This work was supported by National Institute on Drug Abuse grants DA01070 and DA00017 + The correspondence author, E-mail: bentler@ucla.edu 1 1. Introduction Multilevel structural equation modeling (multilevel modeling or MLM for short) has been found to be a useful tec..

Topics: ioral, medical, educational and social sciences (Goldstein, 1987, Bock, 1989). Much
Publisher: Erlbaum
Year: 2003
OAI identifier: oai:CiteSeerX.psu:10.1.1.36.46
Provided by: CiteSeerX
Download PDF:
Sorry, we are unable to provide the full text but you may find it at the following location(s):
  • http://citeseerx.ist.psu.edu/v... (external link)
  • http://www.stat.ucla.edu/paper... (external link)
  • Suggested articles


    To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.