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Finite Mixture Models

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  • Partha Deb

    () (Hunter College and the Graduate Center, CUNY)

Abstract

Finite 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.

Suggested Citation

  • Partha Deb, 2008. "Finite Mixture Models," Summer North American Stata Users' Group Meetings 2008 7, Stata Users Group, revised 28 Aug 2008.
  • Handle: RePEc:boc:nsug08:7
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    File URL: http://repec.org/snasug08/deb_fmm_slides.pdf
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    Cited by:

    1. Nathan, Max, 2014. "Top Team Diversity and Business Performance: Latent Class Analysis for Firms and Cities," IZA Discussion Papers 8462, Institute for the Study of Labor (IZA).
    2. repec:spr:pharme:v:35:y:2017:i:4:d:10.1007_s40273-016-0476-y is not listed on IDEAS

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