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ERIC Number: EJ990383
Record Type: Journal
Publication Date: 2013-Jan
Pages: 20
Abstractor: As Provided
Reference Count: 36
ISSN: ISSN-0033-3123
Global Convergence of the EM Algorithm for Unconstrained Latent Variable Models with Categorical Indicators
Weissman, Alexander
Psychometrika, v78 n1 p134-153 Jan 2013
Convergence of the expectation-maximization (EM) algorithm to a global optimum of the marginal log likelihood function for unconstrained latent variable models with categorical indicators is presented. The sufficient conditions under which global convergence of the EM algorithm is attainable are provided in an information-theoretic context by interpreting the EM algorithm as alternating minimization of the Kullback-Leibler divergence between two convex sets. It is shown that these conditions are satisfied by an unconstrained latent class model, yielding an optimal bound against which more highly constrained models may be compared.
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Publication Type: Journal Articles; Reports - Research
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A