Interpretation and inference in mixture models: Simple MCMC works

Geweke, J

Interpretation and inference in mixture models: Simple MCMC works

Geweke, J

Permalink

Publication Type:: Journal Article
Citation:: Computational Statistics and Data Analysis, 2007, 51 (7), pp. 3529 - 3550
Issue Date:: 2007-04-01

Closed Access

	Filename	Description	Size
	2008008228OK.pdf		2.9 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Geweke, J	en_US
dc.date.issued	2007-04-01	en_US
dc.identifier.citation	Computational Statistics and Data Analysis, 2007, 51 (7), pp. 3529 - 3550	en_US
dc.identifier.issn	0167-9473	en_US
dc.identifier.uri	http://hdl.handle.net/10453/8256
dc.description.abstract	The mixture model likelihood function is invariant with respect to permutation of the components of the mixture. If functions of interest are permutation sensitive, as in classification applications, then interpretation of the likelihood function requires valid inequality constraints and a very large sample may be required to resolve ambiguities. If functions of interest are permutation invariant, as in prediction applications, then there are no such problems of interpretation. Contrary to assessments in some recent publications, simple and widely used Markov chain Monte Carlo (MCMC) algorithms with data augmentation reliably recover the entire posterior distribution. © 2006 Elsevier B.V. All rights reserved.	en_US
dc.relation.ispartof	Computational Statistics and Data Analysis	en_US
dc.relation.isbasedon	10.1016/j.csda.2006.11.026	en_US
dc.subject.classification	Statistics & Probability	en_US
dc.title	Interpretation and inference in mixture models: Simple MCMC works	en_US
dc.type	Journal Article
utslib.citation.volume	7	en_US
utslib.citation.volume	51	en_US
utslib.for	010405 Statistical Theory	en_US
utslib.for	0104 Statistics	en_US
utslib.for	0802 Computation Theory and Mathematics	en_US
utslib.for	1403 Econometrics	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Business
pubs.organisational-group	/University of Technology Sydney/Faculty of Business/Economics Discipline
utslib.copyright.status	closed_access
pubs.issue	7	en_US
pubs.publication-status	Published	en_US
pubs.volume	51	en_US

Abstract:

The mixture model likelihood function is invariant with respect to permutation of the components of the mixture. If functions of interest are permutation sensitive, as in classification applications, then interpretation of the likelihood function requires valid inequality constraints and a very large sample may be required to resolve ambiguities. If functions of interest are permutation invariant, as in prediction applications, then there are no such problems of interpretation. Contrary to assessments in some recent publications, simple and widely used Markov chain Monte Carlo (MCMC) algorithms with data augmentation reliably recover the entire posterior distribution. © 2006 Elsevier B.V. All rights reserved.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/8256