Handle URI:
http://hdl.handle.net/10754/624965
Title:
Stochastic Generalized Method of Moments
Authors:
Yin, Guosheng; Ma, Yanyuan; Liang, Faming; Yuan, Ying
Abstract:
The 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.
Citation:
Yin G, Ma Y, Liang F, Yuan Y (2011) Stochastic Generalized Method of Moments. Journal of Computational and Graphical Statistics 20: 714–727. Available: http://dx.doi.org/10.1198/jcgs.2011.09210.
Publisher:
Informa UK Limited
Journal:
Journal of Computational and Graphical Statistics
KAUST Grant Number:
KUS-C1-016-04
Issue Date:
16-Aug-2011
DOI:
10.1198/jcgs.2011.09210
Type:
Article
ISSN:
1061-8600; 1537-2715
Sponsors:
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).
Appears in Collections:
Publications Acknowledging KAUST Support

Full metadata record

DC FieldValue Language
dc.contributor.authorYin, Guoshengen
dc.contributor.authorMa, Yanyuanen
dc.contributor.authorLiang, Famingen
dc.contributor.authorYuan, Yingen
dc.date.accessioned2017-06-12T13:52:08Z-
dc.date.available2017-06-12T13:52:08Z-
dc.date.issued2011-08-16en
dc.identifier.citationYin G, Ma Y, Liang F, Yuan Y (2011) Stochastic Generalized Method of Moments. Journal of Computational and Graphical Statistics 20: 714–727. Available: http://dx.doi.org/10.1198/jcgs.2011.09210.en
dc.identifier.issn1061-8600en
dc.identifier.issn1537-2715en
dc.identifier.doi10.1198/jcgs.2011.09210en
dc.identifier.urihttp://hdl.handle.net/10754/624965-
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
dc.description.sponsorshipWe 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).en
dc.publisherInforma UK Limiteden
dc.subjectGeneralized linear modelen
dc.subjectGibbs samplingen
dc.subjectIterative monte carloen
dc.subjectMarkov chain monte carloen
dc.subjectMetropolis algorithmen
dc.subjectMoment conditionen
dc.titleStochastic Generalized Method of Momentsen
dc.typeArticleen
dc.identifier.journalJournal of Computational and Graphical Statisticsen
dc.contributor.institutionDepartment of Statistics and Actuarial Science, The University of Hong Kong, Pokfulam Road, , Hong Kongen
dc.contributor.institutionDepartment of Statistics, Texas A and M University, College Station, TX 77843, , United Statesen
dc.contributor.institutionDepartment of Biostatistics-Unit 1411, M. D. Anderson Cancer Center, The University of Texas, Houston, TX 77230, , , United Statesen
kaust.grant.numberKUS-C1-016-04en
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