Journal ArticleDOI
Application of the EM Method A Study of Maximum Likelihood Estimation of Multiple Indicator and Factor Analysis Models
Ronald Schoenberg,Carol Richtand +1 more
TLDR
The EM (Estimation-Maximization) algorithm is exploited to provide maximum likelihood estimates of the parameters of multiple indicator/factor analysis models to reduce considerably the storage and computational burden of such estimation.Abstract:
The EM (Estimation-Maximization) algorithm is exploited to provide maximum likelihood estimates of the parameters of multiple indicator/factor analysis models. This method reduces considerably the storage and computational burden of such estimation. A computer program in BASIC language that performs the computations is listed in an appendix. The specification of correlated errors is also provided for in this application of the method.read more
Citations
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Discovering Causal Structure: Artificial Intelligence, Philosophy of Science, and Statistical Modeling
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Finite mixtures in confirmatory factor-analysis models
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Discovery and representation of causal relationships in MIS research: a methodological framework
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Uniqueness does not Imply Identification A Note on Confirmatory Factor Analysis
TL;DR: This paper showed that conditions originally presented by Howe (1955) and later by Joreskog (1979) are not sufficient to identify confirmatory factor analysis models, and showed that these conditions are insufficient.
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Disability and pain management methods of Taiwanese arthritic older patients.
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References
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Journal ArticleDOI
An Efficient Method of Estimating Seemingly Unrelated Regressions and Tests for Aggregation Bias
TL;DR: In this paper, a method of estimating the parameters of a set of regression equations is reported which involves application of Aitken's generalized least-squares to the whole system of equations.
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A general method for analysis of covariance structures
TL;DR: In this article, a general multivariate normal distribution with a general parametric form of the mean vector and the variance-covariance matrix is proposed, where any parameter of the model may be fixed, free or constrained to be equal to other parameters.
Journal ArticleDOI
EM Algorithms for ML Factor Analysis.
TL;DR: In this paper, the authors present EM algorithms for both exploratory and confirmatory models for maximum likelihood factor analysis, which are essentially the same for both cases and involve only simple least squares regression operations; the largest matrix inversion required is for aq ×q symmetric matrix whereq is the matrix of factors.
Journal ArticleDOI
Linear structural equations with latent variables
Peter M. Bentler,David G. Weeks +1 more
TL;DR: In this article, an interdependent multivariate linear relations model based on manifest, measured variables as well as unmeasured and unmeasurable latent variables is developed, which is designed to accommodate a wider range of applications via its structural equations, mean structure, covariance structure, and constraints on parameters.