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Conference Paper: Bayesian generalized method of moments
Title | Bayesian generalized method of moments |
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Authors | |
Keywords | Bayesian inference Estimation efficiency Generalized estimating equation Generalized linear model Gibbs sampling |
Issue Date | 2010 |
Publisher | American Statistical Association. |
Citation | The 2010 Joint Statistical Meetings (JSM 2010), Vancouver, BC., 31 July-5 August 2010 How to Cite? |
Abstract | We propose the Bayesian generalized method of moments (GMM), which is particularly useful when likelihood-based methods are difficult. By deriving 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 exponentiate it to substitute for the usual likelihood. After specifying the prior distributions, 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. |
Description | Bayesian Analysis Invited Session — Invited Papers : Abstract - #305960 |
Persistent Identifier | http://hdl.handle.net/10722/241359 |
DC Field | Value | Language |
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dc.contributor.author | Yin, G | - |
dc.date.accessioned | 2017-06-08T04:25:00Z | - |
dc.date.available | 2017-06-08T04:25:00Z | - |
dc.date.issued | 2010 | - |
dc.identifier.citation | The 2010 Joint Statistical Meetings (JSM 2010), Vancouver, BC., 31 July-5 August 2010 | - |
dc.identifier.uri | http://hdl.handle.net/10722/241359 | - |
dc.description | Bayesian Analysis Invited Session — Invited Papers : Abstract - #305960 | - |
dc.description.abstract | We propose the Bayesian generalized method of moments (GMM), which is particularly useful when likelihood-based methods are difficult. By deriving 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 exponentiate it to substitute for the usual likelihood. After specifying the prior distributions, 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.language | eng | - |
dc.publisher | American Statistical Association. | - |
dc.relation.ispartof | Joint Statistical Meetings, Vancouver, JSM 2010 | - |
dc.subject | Bayesian inference | - |
dc.subject | Estimation efficiency | - |
dc.subject | Generalized estimating equation | - |
dc.subject | Generalized linear model | - |
dc.subject | Gibbs sampling | - |
dc.title | Bayesian generalized method of moments | - |
dc.type | Conference_Paper | - |
dc.identifier.email | Yin, G: gyin@hku.hk | - |
dc.identifier.authority | Yin, G=rp00831 | - |
dc.identifier.hkuros | 177213 | - |
dc.publisher.place | United States | - |