The EM Algorithm for Latent Class Analysis with Equality Constraints
- 1 June 1992
- journal article
- Published by Cambridge University Press (CUP) in Psychometrika
- Vol. 57 (2) , 261-269
- https://doi.org/10.1007/bf02294508
Abstract
The EM algorithm is a popular iterative method for estimating parameters in the latent class model where at each step the unknown parameters can be estimated simply as weighted sums of some latent proportions. The algorithm may also be used when some parameters are constrained to equal given constants or each other. It is shown that in the general case with equality constraints, the EM algorithm is not simple to apply because a nonlinear equation has to be solved. This problem arises, mainly, when equality constraints are defined over probabilities in different combinations of variables and latent classes. A simple condition is given in which, although probabilities in different variable-latent class combinations are constrained to be equal, the EM algorithm is still simple to apply.Keywords
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