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- Publisher Website: 10.1198/jcgs.2011.09210
- Scopus: eid_2-s2.0-80053377942
- PMID: 22375093
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Article: Stochastic generalized method of moments
Title | Stochastic generalized method of moments | ||||||||||
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Authors | |||||||||||
Keywords | Generalized linear model Gibbs sampling Iterative monte carlo Markov chain monte carlo Metropolis algorithm Moment condition | ||||||||||
Issue Date | 2011 | ||||||||||
Publisher | American Statistical Association. | ||||||||||
Citation | Journal Of Computational And Graphical Statistics, 2011, v. 20 n. 3, p. 714-727 How to Cite? | ||||||||||
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. | ||||||||||
Persistent Identifier | http://hdl.handle.net/10722/139718 | ||||||||||
ISSN | 2023 Impact Factor: 1.4 2023 SCImago Journal Rankings: 1.530 | ||||||||||
PubMed Central ID | |||||||||||
ISI Accession Number ID |
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 Field | Value | Language |
---|---|---|
dc.contributor.author | Yin, G | en_HK |
dc.contributor.author | Ma, Y | en_HK |
dc.contributor.author | Liang, F | en_HK |
dc.contributor.author | Yuan, Y | en_HK |
dc.date.accessioned | 2011-09-23T05:54:46Z | - |
dc.date.available | 2011-09-23T05:54:46Z | - |
dc.date.issued | 2011 | en_HK |
dc.identifier.citation | Journal Of Computational And Graphical Statistics, 2011, v. 20 n. 3, p. 714-727 | en_HK |
dc.identifier.issn | 1061-8600 | en_HK |
dc.identifier.uri | http://hdl.handle.net/10722/139718 | - |
dc.description.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. | en_HK |
dc.language | eng | en_US |
dc.publisher | American Statistical Association. | en_US |
dc.relation.ispartof | Journal of Computational and Graphical Statistics | en_HK |
dc.subject | Generalized linear model | en_HK |
dc.subject | Gibbs sampling | en_HK |
dc.subject | Iterative monte carlo | en_HK |
dc.subject | Markov chain monte carlo | en_HK |
dc.subject | Metropolis algorithm | en_HK |
dc.subject | Moment condition | en_HK |
dc.title | Stochastic generalized method of moments | en_HK |
dc.type | Article | en_HK |
dc.identifier.email | Yin, G: gyin@hku.hk | en_HK |
dc.identifier.authority | Yin, G=rp00831 | en_HK |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1198/jcgs.2011.09210 | en_HK |
dc.identifier.pmid | 22375093 | - |
dc.identifier.pmcid | PMC3286612 | - |
dc.identifier.scopus | eid_2-s2.0-80053377942 | en_HK |
dc.identifier.hkuros | 195640 | en_US |
dc.relation.references | http://www.scopus.com/mlt/select.url?eid=2-s2.0-80053377942&selection=ref&src=s&origin=recordpage | en_HK |
dc.identifier.volume | 20 | en_HK |
dc.identifier.issue | 3 | en_HK |
dc.identifier.spage | 714 | en_HK |
dc.identifier.epage | 727 | en_HK |
dc.identifier.isi | WOS:000296073500012 | - |
dc.publisher.place | United States | en_HK |
dc.identifier.scopusauthorid | Yin, G=8725807500 | en_HK |
dc.identifier.scopusauthorid | Ma, Y=8908626500 | en_HK |
dc.identifier.scopusauthorid | Liang, F=7201916078 | en_HK |
dc.identifier.scopusauthorid | Yuan, Y=7402709174 | en_HK |
dc.identifier.issnl | 1061-8600 | - |