IDEAS home Printed from
   My bibliography  Save this paper

Finite Mixture Models


  • Partha Deb

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


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

    Download full text from publisher

    File URL:
    Download Restriction: no


    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.

    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

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:


    Access and download statistics


    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:boc:nsug08:7. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Christopher F Baum). General contact details of provider: .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.