Finite Mixture Modeling with Mixture Outcomes Using the EM Algorithm

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ID: 292319
1999
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Abstract
Summary. This paper discusses the analysis of an extended finite mixture model where the latent classes corresponding to the mixture components for one set of observed variables influence a second set of observed variables. The research is motivated by a repeated measurement study using a random coefficient model to assess the influence of latent growth trajectory class membership on the probability of a binary disease outcome. More generally, this model can be seen as a combination of latent class modeling and conventional mixture modeling. The EM algorithm is used for estimation. As an illustration, a random‐coefficient growth model for the prediction of alcohol dependence from three latent classes of heavy alcohol use trajectories among young adults is analyzed.
Reference Key
openalex_W2057964179 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Bengt Muthén, Kerby Shedden
Journal biometrics
Year 1999
DOI
10.1111/j.0006-341x.1999.00463.x
URL
https://doi.org/10.1111/j.0006-341x.1999.00463.x
Keywords Keywords not found

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