Finite Mixture Models
AbstractFinite mixture models provide a natural way of modeling continuous or discrete outcomes that are observed from populations consisting of a finite number of homogeneous subpopulations. Applications of finite mixture models are abundant in the social and behavioral sciences, biological and environmental sciences, engineering and finance. Such models have a natural representation of heterogeneity in a finite, usually small, number of latent classes, each of which may be regarded as a type. More generally, the finite mixture model can be shown to approximate any unknown distribution under suitable regularity conditions. The Stata package -fmm- implements a maximum likelihood estimator for a class of finite mixture models. In this talk, I will begin by introducing finite mixture models using a number of examples and discuss issues of estimation, testing and model selection. I will then describe estimation using fmm, calculations of predictions, marginal effects, and posterior class probabilities, and illustrate these using examples from econometrics and finance.
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Bibliographic InfoPaper provided by Stata Users Group in its series Summer North American Stata Users' Group Meetings 2008 with number 7.
Date of creation: 29 Jul 2008
Date of revision: 28 Aug 2008
This paper has been announced in the following NEP Reports:
- NEP-ALL-2008-09-05 (All new papers)
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