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Article: Stochastic generalized method of moments

TitleStochastic generalized method of moments
Authors
KeywordsGeneralized linear model
Gibbs sampling
Iterative monte carlo
Markov chain monte carlo
Metropolis algorithm
Moment condition
Issue Date2011
PublisherAmerican Statistical Association.
Citation
Journal Of Computational And Graphical Statistics, 2011, v. 20 n. 3, p. 714-727 How to Cite?
AbstractThe generalized method of moments (GMM) is a very popular estimation and inference procedure based on moment conditions. When likelihood-based methods are difficult to implement, one can often derive various moment conditions and construct the GMM objective function. However, minimization of the objective function in the GMM may be challenging, especially over a large parameter space. Due to the special structure of the GMM, we propose a new sampling-based algorithm, the stochastic GMM sampler, which replaces the multivariate minimization problem by a series of conditional sampling procedures. We develop the theoretical properties of the proposed iterative Monte Carlo method, and demonstrate its superior performance over other GMM estimation procedures in simulation studies. As an illustration, we apply the stochastic GMM sampler to a Medfly life longevity study. Supplemental materials for the article are available online. © 2011 American Statistical Association.
Persistent Identifierhttp://hdl.handle.net/10722/139718
ISSN
2015 Impact Factor: 1.755
2015 SCImago Journal Rankings: 2.321
PubMed Central ID
ISI Accession Number ID
Funding AgencyGrant Number
Research Grants Council of Hong Kong
US NSFDMS-1007457
CMMI-0926803
King Abdullah University of Science and TechnologyKUS-C1-016-04
U.S. National Cancer InstituteR01CA154591-01A1
Funding Information:

We thank the referees, associate editor, and editor for many insightful suggestions which strengthened the work immensely. Yin's research was supported by a grant from the Research Grants Council of Hong Kong, Ma's research was supported by a US NSF grant, Liang's research was supported by grants from US NSF (DMS-1007457 and CMMI-0926803) and King Abdullah University of Science and Technology (KUS-C1-016-04), and Yuan's research was supported by a U.S. National Cancer Institute R01 grant (R01CA154591-01A1).

References

 

DC FieldValueLanguage
dc.contributor.authorYin, Gen_HK
dc.contributor.authorMa, Yen_HK
dc.contributor.authorLiang, Fen_HK
dc.contributor.authorYuan, Yen_HK
dc.date.accessioned2011-09-23T05:54:46Z-
dc.date.available2011-09-23T05:54:46Z-
dc.date.issued2011en_HK
dc.identifier.citationJournal Of Computational And Graphical Statistics, 2011, v. 20 n. 3, p. 714-727en_HK
dc.identifier.issn1061-8600en_HK
dc.identifier.urihttp://hdl.handle.net/10722/139718-
dc.description.abstractThe generalized method of moments (GMM) is a very popular estimation and inference procedure based on moment conditions. When likelihood-based methods are difficult to implement, one can often derive various moment conditions and construct the GMM objective function. However, minimization of the objective function in the GMM may be challenging, especially over a large parameter space. Due to the special structure of the GMM, we propose a new sampling-based algorithm, the stochastic GMM sampler, which replaces the multivariate minimization problem by a series of conditional sampling procedures. We develop the theoretical properties of the proposed iterative Monte Carlo method, and demonstrate its superior performance over other GMM estimation procedures in simulation studies. As an illustration, we apply the stochastic GMM sampler to a Medfly life longevity study. Supplemental materials for the article are available online. © 2011 American Statistical Association.en_HK
dc.languageengen_US
dc.publisherAmerican Statistical Association.en_US
dc.relation.ispartofJournal of Computational and Graphical Statisticsen_HK
dc.subjectGeneralized linear modelen_HK
dc.subjectGibbs samplingen_HK
dc.subjectIterative monte carloen_HK
dc.subjectMarkov chain monte carloen_HK
dc.subjectMetropolis algorithmen_HK
dc.subjectMoment conditionen_HK
dc.titleStochastic generalized method of momentsen_HK
dc.typeArticleen_HK
dc.identifier.emailYin, G: gyin@hku.hken_HK
dc.identifier.authorityYin, G=rp00831en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1198/jcgs.2011.09210en_HK
dc.identifier.pmid22375093-
dc.identifier.pmcidPMC3286612-
dc.identifier.scopuseid_2-s2.0-80053377942en_HK
dc.identifier.hkuros195640en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-80053377942&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume20en_HK
dc.identifier.issue3en_HK
dc.identifier.spage714en_HK
dc.identifier.epage727en_HK
dc.identifier.isiWOS:000296073500012-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridYin, G=8725807500en_HK
dc.identifier.scopusauthoridMa, Y=8908626500en_HK
dc.identifier.scopusauthoridLiang, F=7201916078en_HK
dc.identifier.scopusauthoridYuan, Y=7402709174en_HK

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