Hyper-EM for large recursive models of categorical variables

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When a recursive model is of a manageable size on a computing machine to use, whether it involves latent variables or not matters little. Application of an EM algorithm for the model is straightforward. But when the model is large enough to reach or exceed the storage space and contains latent variables, parameter estimation for the model looks almost infeasible. In this paper, a new EM approach is proposed for large recursive models and its convergence is proved. A key idea behind it is (1) that we partition a model into several submodels in such a way that the variables of submodel A, say, are conditionally independent of the other Variables in the model given that the values of the variables of submodel A which are involved in any other submodels are known and (2) that the likelihood function for the whole model is factorized by the submodels. (C) 2000 Elsevier Science B.V. All rights reserved.
Publisher
ELSEVIER SCIENCE BV
Issue Date
2000-06
Language
English
Article Type
Article
Keywords

CONTINGENCY-TABLES; MAXIMUM-LIKELIHOOD; MARKOV-FIELDS; MISSING DATA; ALGORITHM; SYSTEMS; INDEPENDENCE

Citation

COMPUTATIONAL STATISTICS DATA ANALYSIS, v.33, no.4, pp.401 - 424

ISSN
0167-9473
URI
http://hdl.handle.net/10203/75472
Appears in Collection
MA-Journal Papers(저널논문)
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