Finite Mixture Distribution, Sequential Likelihood, and the EM Algorithm
Peter Arcidiacono and
No 00-16, Working Papers from Duke University, Department of Economics
The use of finite mixture distributions to control for unobserved heterogeneity has become increasingly popular among those estimating dynamic discrete choice models. One of the barriers to using mixture models is that parameters that could previously be estimated in stages must now be estimated jointly: using mixture distributions destroys any additive separability of the log likelihood function. The EM algorithm reintroduces additive separability, however, thus allowing the option of estimating parameters sequentially during each maximization step. We show that, relative to full information maximum likelihood, the EM algorithm with sequential maximization (ESM) can generate large computational savings with little loss of efficiency.
JEL-codes: C13 C61 D90 (search for similar items in EconPapers)
References: Add references at CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
http://www.econ.duke.edu/Papers/Abstracts00/abstract.00.16.html main text
Journal Article: Finite Mixture Distributions, Sequential Likelihood and the EM Algorithm (2003)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:duk:dukeec:00-16
Access Statistics for this paper
More papers in Working Papers from Duke University, Department of Economics Department of Economics Duke University 213 Social Sciences Building Box 90097 Durham, NC 27708-0097.
Bibliographic data for series maintained by Department of Economics Webmaster ().