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Article: Bayesian generalized method of moments

TitleBayesian generalized method of moments
Authors
KeywordsBayesian inference
Correlated data
Estimation efficiency
Generalized estimating equation
Generalized linear model
Issue Date2009
PublisherInternational Society for Bayesian Analysis. The Journal's web site is located at http://ba.stat.cmu.edu/
Citation
Bayesian Analysis, 2009, v. 4 n. 2, p. 191-208 How to Cite?
AbstractWe propose the Bayesian generalized method of moments (GMM), which is particularly useful when likelihood-based methods are di±cult. By de-riving the moments and concatenating them together, we build up a weighted quadratic objective function in the GMM framework. As in a normal density function, we take the negative GMM quadratic function divided by two and ex-ponentiate it to substitute for the usual likelihood. After specifying the prior dis-tributions, we apply the Markov chain Monte Carlo procedure to sample from the posterior distribution. We carry out simulation studies to examine the proposed Bayesian GMM procedure, and illustrate it with a real data example.
Persistent Identifierhttp://hdl.handle.net/10722/139730
ISSN
2015 Impact Factor: 1.087
2015 SCImago Journal Rankings: 1.559
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorYin, Gen_US
dc.date.accessioned2011-09-23T05:54:49Z-
dc.date.available2011-09-23T05:54:49Z-
dc.date.issued2009en_US
dc.identifier.citationBayesian Analysis, 2009, v. 4 n. 2, p. 191-208en_US
dc.identifier.issn1931-6690-
dc.identifier.urihttp://hdl.handle.net/10722/139730-
dc.description.abstractWe propose the Bayesian generalized method of moments (GMM), which is particularly useful when likelihood-based methods are di±cult. By de-riving the moments and concatenating them together, we build up a weighted quadratic objective function in the GMM framework. As in a normal density function, we take the negative GMM quadratic function divided by two and ex-ponentiate it to substitute for the usual likelihood. After specifying the prior dis-tributions, we apply the Markov chain Monte Carlo procedure to sample from the posterior distribution. We carry out simulation studies to examine the proposed Bayesian GMM procedure, and illustrate it with a real data example.-
dc.languageengen_US
dc.publisherInternational Society for Bayesian Analysis. The Journal's web site is located at http://ba.stat.cmu.edu/en_US
dc.relation.ispartofBayesian Analysisen_US
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectBayesian inference-
dc.subjectCorrelated data-
dc.subjectEstimation efficiency-
dc.subjectGeneralized estimating equation-
dc.subjectGeneralized linear model-
dc.titleBayesian generalized method of momentsen_US
dc.typeArticleen_US
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1931-6690&volume=4&issue=2&spage=191&epage=208&date=2009&atitle=Bayesian+generalized+method+of+moments-
dc.identifier.emailYin, G: gyin@hku.hken_US
dc.identifier.authorityYin, G=rp00831en_US
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1214/09-BA407-
dc.identifier.scopuseid_2-s2.0-79952597899-
dc.identifier.hkuros195701en_US
dc.identifier.volume4en_US
dc.identifier.issue2en_US
dc.identifier.spage191en_US
dc.identifier.epage208en_US
dc.identifier.isiWOS:000273483400001-
dc.identifier.citeulike11835148-

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