Finite mixture distributions, sequential likelihood and the EM algorithm

dc.contributor.author

Arcidiacono, P

dc.contributor.author

Jones, JB

dc.date.accessioned

2010-03-09T15:27:03Z

dc.date.issued

2003-01-01

dc.description.abstract

A popular way to account for unobserved heterogeneity is to assume that the data are drawn from a finite mixture distribution. A barrier to using finite 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. We show, however, that an extension of the EM algorithm reintroduces additive separability, thus allowing one to estimate parameters sequentially during each maximization step. In establishing this result, we develop a broad class of estimators for mixture models. Returning to the likelihood problem, we show that, relative to full information maximum likelihood, our sequential estimator can generate large computational savings with little loss of efficiency.

dc.format.mimetype

application/pdf

dc.identifier.issn

0012-9682

dc.identifier.uri

https://hdl.handle.net/10161/1873

dc.language.iso

en_US

dc.publisher

The Econometric Society

dc.relation.ispartof

Econometrica

dc.title

Finite mixture distributions, sequential likelihood and the EM algorithm

dc.type

Journal article

pubs.begin-page

933

pubs.end-page

946

pubs.issue

3

pubs.organisational-group

Duke

pubs.organisational-group

Duke Population Research Center

pubs.organisational-group

Duke Population Research Institute

pubs.organisational-group

Economics

pubs.organisational-group

Sanford School of Public Policy

pubs.organisational-group

Trinity College of Arts & Sciences

pubs.publication-status

Published

pubs.volume

71

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