File Download
  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Non-iterative sampling-based Bayesian methods for identifying changepoints in the sequence of cases of Haemolytic uraemic syndrome

TitleNon-iterative sampling-based Bayesian methods for identifying changepoints in the sequence of cases of Haemolytic uraemic syndrome
Authors
Issue Date2009
PublisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/csda
Citation
Computational Statistics And Data Analysis, 2009, v. 53 n. 9, p. 3314-3323 How to Cite?
AbstractDiarrhoea-associated Haemolytic Uraemic syndrome (HUS) is a disease that affects the kidneys and other organs. Motivated by the annual number of cases of HUS collected in Birmingham and Newcastle of England, respectively, from 1970 to 1989, we consider Bayesian changepoint analysis with specific attention to Poisson changepoint models. For changepoint models with unknown number of changepoints, we propose a new non-iterative Bayesian sampling approach (called exact IBF sampling), which completely avoids the problem of convergence and slow convergence associated with iterative Markov chain Monte Carlo (MCMC) methods. The idea is to first utilize the sampling inverse Bayes formula (IBF) to derive the conditional distribution of the latent data given the observed data, and then to draw iid samples from the complete-data posterior distribution. For the purpose of selecting the appropriate model (or determining the number of changepoints), we develop two alternative formulae to exactly calculate marginal likelihood (or Bayes factor) by using the exact IBF output and the point-wise IBF, respectively. The HUS data are re-analyzed using the proposed methods. Simulations are implemented to validate the performance of the proposed methods. © 2009 Elsevier B.V. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/82751
ISSN
2015 Impact Factor: 1.179
2015 SCImago Journal Rankings: 1.283
PubMed Central ID
ISI Accession Number ID
Funding AgencyGrant Number
US National Cancer InstituteCA106767
CA119758
University of Hong Kong
Funding Information:

We are grateful to the Editor, an Associate Editor and three referees for their constructive comments and suggestions. G.L. Tian and M. Tan's research was supported in part by US National Cancer Institute grants CA106767 and CA119758. The research of KW Ng was partially supported by a research grant of the University of Hong Kong. Special thanks should go to one referee for drawing our attention to several recent papers on multiple change-point problems.

References

 

DC FieldValueLanguage
dc.contributor.authorTian, GLen_HK
dc.contributor.authorNg, KWen_HK
dc.contributor.authorLi, KCen_HK
dc.contributor.authorTan, Men_HK
dc.date.accessioned2010-09-06T08:33:00Z-
dc.date.available2010-09-06T08:33:00Z-
dc.date.issued2009en_HK
dc.identifier.citationComputational Statistics And Data Analysis, 2009, v. 53 n. 9, p. 3314-3323en_HK
dc.identifier.issn0167-9473en_HK
dc.identifier.urihttp://hdl.handle.net/10722/82751-
dc.description.abstractDiarrhoea-associated Haemolytic Uraemic syndrome (HUS) is a disease that affects the kidneys and other organs. Motivated by the annual number of cases of HUS collected in Birmingham and Newcastle of England, respectively, from 1970 to 1989, we consider Bayesian changepoint analysis with specific attention to Poisson changepoint models. For changepoint models with unknown number of changepoints, we propose a new non-iterative Bayesian sampling approach (called exact IBF sampling), which completely avoids the problem of convergence and slow convergence associated with iterative Markov chain Monte Carlo (MCMC) methods. The idea is to first utilize the sampling inverse Bayes formula (IBF) to derive the conditional distribution of the latent data given the observed data, and then to draw iid samples from the complete-data posterior distribution. For the purpose of selecting the appropriate model (or determining the number of changepoints), we develop two alternative formulae to exactly calculate marginal likelihood (or Bayes factor) by using the exact IBF output and the point-wise IBF, respectively. The HUS data are re-analyzed using the proposed methods. Simulations are implemented to validate the performance of the proposed methods. © 2009 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.titleNon-iterative sampling-based Bayesian methods for identifying changepoints in the sequence of cases of Haemolytic uraemic syndromeen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0167-9473&volume=53&issue=9&spage=3314&epage=3323.&date=2009&atitle=Non-iterative+Sampling-based+Bayesian+Methods+for+Identifying+Changepoints+in+the+Sequence+of+Cases+of+Haemolytic+Uraemic+Syndromeen_HK
dc.identifier.emailTian, GL: gltian@hku.hken_HK
dc.identifier.emailNg, KW: kaing@hkucc.hku.hken_HK
dc.identifier.authorityTian, GL=rp00789en_HK
dc.identifier.authorityNg, KW=rp00765en_HK
dc.description.naturelink_to_OA_fulltext-
dc.identifier.doi10.1016/j.csda.2009.02.006en_HK
dc.identifier.pmid20161336-
dc.identifier.pmcidPMC2678871-
dc.identifier.scopuseid_2-s2.0-64749090747en_HK
dc.identifier.hkuros163566en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-64749090747&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume53en_HK
dc.identifier.issue9en_HK
dc.identifier.spage3314en_HK
dc.identifier.epage3323en_HK
dc.identifier.isiWOS:000266381800006-
dc.publisher.placeNetherlandsen_HK
dc.identifier.scopusauthoridTian, GL=25621549400en_HK
dc.identifier.scopusauthoridNg, KW=7403178774en_HK
dc.identifier.scopusauthoridLi, KC=7404989239en_HK
dc.identifier.scopusauthoridTan, M=7401464681en_HK

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats