File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Hierarchical models for repeated binary data using the IBF sampler

TitleHierarchical models for repeated binary data using the IBF sampler
Authors
KeywordsBayesian computation
Gibbs sampler
Inverse Bayes formulae
MCMC
Monte Carlo EM algorithm
Issue Date2006
PublisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/csda
Citation
Computational Statistics And Data Analysis, 2006, v. 50 n. 5, p. 1272-1286 How to Cite?
AbstractHierarchical models have emerged as a promising tool for the analysis of repeated binary data. However, the computational complexity in these models have limited their applications in practice. Several approaches have been proposed in the literature to overcome the computational difficulties including maximum likelihood estimation from a frequentist perspective (e.g., J. Amer. Statist. Assoc. 89 (1994) 330-335) and Markov chain Monte Carlo (MCMC) methods from a Bayesian perspective (e.g., Generalized Linear Models: A Bayesian Perspective, Marcel Dekker, New York, pp. 113-131). Although MCMC methods provide the whole posterior of the parameter of interest, the convergence diagnostics problem of the Markov chain and the slow convergence problem owing to the introduction of too many Gaussian latent variables are still unresolved. Recently, Tan et al. (Statist. Sinica 13 (2003) 625-639) proposed a noniterative sampling approach, the inverse Bayes formulae (IBF) sampler, for computing posteriors in the structure of EM algorithm. This article develops the IBF sampler in the structure of Monte Carlo EM (MCEM) for the hierarchical model with repeated binary data for which current methods encounter difficulty. An efficient IBF sampler is implemented by utilizing the estimated posterior modes obtained via MCEM algorithm. The proposed method generates independent and identically distributed (iid) samples approximately from the observed posterior distribution and thus alleviates the convergence problem associated with the MCMC methods. In addition, the slow convergence problem in Gibbs sampler can be bypassed in the noniterative IBF sampler via running some fast EM-type algorithm. Real datasets from six cities children's wheeze study and children's ear fluid study illustrate the proposed methods. © 2004 Elsevier B.V. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/82927
ISSN
2015 Impact Factor: 1.179
2015 SCImago Journal Rankings: 1.283
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorTan, Men_HK
dc.contributor.authorTian, GLen_HK
dc.contributor.authorWang Ng, Ken_HK
dc.date.accessioned2010-09-06T08:34:58Z-
dc.date.available2010-09-06T08:34:58Z-
dc.date.issued2006en_HK
dc.identifier.citationComputational Statistics And Data Analysis, 2006, v. 50 n. 5, p. 1272-1286en_HK
dc.identifier.issn0167-9473en_HK
dc.identifier.urihttp://hdl.handle.net/10722/82927-
dc.description.abstractHierarchical models have emerged as a promising tool for the analysis of repeated binary data. However, the computational complexity in these models have limited their applications in practice. Several approaches have been proposed in the literature to overcome the computational difficulties including maximum likelihood estimation from a frequentist perspective (e.g., J. Amer. Statist. Assoc. 89 (1994) 330-335) and Markov chain Monte Carlo (MCMC) methods from a Bayesian perspective (e.g., Generalized Linear Models: A Bayesian Perspective, Marcel Dekker, New York, pp. 113-131). Although MCMC methods provide the whole posterior of the parameter of interest, the convergence diagnostics problem of the Markov chain and the slow convergence problem owing to the introduction of too many Gaussian latent variables are still unresolved. Recently, Tan et al. (Statist. Sinica 13 (2003) 625-639) proposed a noniterative sampling approach, the inverse Bayes formulae (IBF) sampler, for computing posteriors in the structure of EM algorithm. This article develops the IBF sampler in the structure of Monte Carlo EM (MCEM) for the hierarchical model with repeated binary data for which current methods encounter difficulty. An efficient IBF sampler is implemented by utilizing the estimated posterior modes obtained via MCEM algorithm. The proposed method generates independent and identically distributed (iid) samples approximately from the observed posterior distribution and thus alleviates the convergence problem associated with the MCMC methods. In addition, the slow convergence problem in Gibbs sampler can be bypassed in the noniterative IBF sampler via running some fast EM-type algorithm. Real datasets from six cities children's wheeze study and children's ear fluid study illustrate the proposed methods. © 2004 Elsevier B.V. All rights reserved.en_HK
dc.languageengen_HK
dc.publisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/csdaen_HK
dc.relation.ispartofComputational Statistics and Data Analysisen_HK
dc.rightsComputational Statistics & Data Analysis . Copyright © Elsevier BV.en_HK
dc.subjectBayesian computationen_HK
dc.subjectGibbs sampleren_HK
dc.subjectInverse Bayes formulaeen_HK
dc.subjectMCMCen_HK
dc.subjectMonte Carlo EM algorithmen_HK
dc.titleHierarchical models for repeated binary data using the IBF sampleren_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0167-9473&volume=50&issue=5&spage=1272&epage=1286&date=2006&atitle=Hierarchical+models+for+repeated+binary+data+using+the+IBF+sampleren_HK
dc.identifier.emailTian, GL: gltian@hku.hken_HK
dc.identifier.emailWang Ng, K: kaing@hkucc.hku.hken_HK
dc.identifier.authorityTian, GL=rp00789en_HK
dc.identifier.authorityWang Ng, K=rp00765en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.csda.2004.12.006en_HK
dc.identifier.scopuseid_2-s2.0-27644554888en_HK
dc.identifier.hkuros138165en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-27644554888&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume50en_HK
dc.identifier.issue5en_HK
dc.identifier.spage1272en_HK
dc.identifier.epage1286en_HK
dc.identifier.isiWOS:000234940200007-
dc.publisher.placeNetherlandsen_HK
dc.identifier.scopusauthoridTan, M=7401464681en_HK
dc.identifier.scopusauthoridTian, GL=25621549400en_HK
dc.identifier.scopusauthoridWang Ng, K=7403178774en_HK
dc.identifier.citeulike812802-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats