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Article: Bayesian analysis of wandering vector models for displaying ranking data

TitleBayesian analysis of wandering vector models for displaying ranking data
Authors
KeywordsBayesian approach
Gibbs sampling
Marginal likelihood
Ranking data
Wandering vector model
Issue Date2001
PublisherAcademia Sinica, Institute of Statistical Science. The Journal's web site is located at http://www.stat.sinica.edu.tw/statistica/
Citation
Statistica Sinica, 2001, v. 11 n. 2, p. 445-461 How to Cite?
AbstractIn a process of examining k objects, each judge provides a ranking of them. The aim of this paper is to investigate a probabilistic model for ranking data - the wandering vector model. The model represents objects by points in a d-dimensional space, and the judges are represented by latent vectors emanating from the origin in the same space. Each judge samples a vector from a multivariate normal distribution; given this vector, the judge's utility assigned to an object is taken to be the length of the orthogonal projection of the object point onto the judge vector, plus a normally distributed random error. The ordering of the k utilities given by the judge determines the judge's ranking. A Bayesian approach and the Gibbs sampling technique are used for parameter estimation. The method of computing the marginal likelihood proposed by Chib (1995) is used to select the dimensionality of the model. Simulations are done to demonstrate the proposed estimation and model selection method. We then analyze the Goldberg data, in which 10 occupations are ranked according to the degree of social prestige.
Persistent Identifierhttp://hdl.handle.net/10722/45349
ISSN
2015 Impact Factor: 0.838
2015 SCImago Journal Rankings: 2.292
References

 

DC FieldValueLanguage
dc.contributor.authorYu, PLHen_HK
dc.contributor.authorChan, LKYen_HK
dc.date.accessioned2007-10-30T06:23:30Z-
dc.date.available2007-10-30T06:23:30Z-
dc.date.issued2001en_HK
dc.identifier.citationStatistica Sinica, 2001, v. 11 n. 2, p. 445-461en_HK
dc.identifier.issn1017-0405en_HK
dc.identifier.urihttp://hdl.handle.net/10722/45349-
dc.description.abstractIn a process of examining k objects, each judge provides a ranking of them. The aim of this paper is to investigate a probabilistic model for ranking data - the wandering vector model. The model represents objects by points in a d-dimensional space, and the judges are represented by latent vectors emanating from the origin in the same space. Each judge samples a vector from a multivariate normal distribution; given this vector, the judge's utility assigned to an object is taken to be the length of the orthogonal projection of the object point onto the judge vector, plus a normally distributed random error. The ordering of the k utilities given by the judge determines the judge's ranking. A Bayesian approach and the Gibbs sampling technique are used for parameter estimation. The method of computing the marginal likelihood proposed by Chib (1995) is used to select the dimensionality of the model. Simulations are done to demonstrate the proposed estimation and model selection method. We then analyze the Goldberg data, in which 10 occupations are ranked according to the degree of social prestige.en_HK
dc.format.extent342219 bytes-
dc.format.extent1783 bytes-
dc.format.mimetypeapplication/pdf-
dc.format.mimetypetext/plain-
dc.languageengen_HK
dc.publisherAcademia Sinica, Institute of Statistical Science. The Journal's web site is located at http://www.stat.sinica.edu.tw/statistica/en_HK
dc.relation.ispartofStatistica Sinicaen_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectBayesian approachen_HK
dc.subjectGibbs samplingen_HK
dc.subjectMarginal likelihooden_HK
dc.subjectRanking dataen_HK
dc.subjectWandering vector modelen_HK
dc.titleBayesian analysis of wandering vector models for displaying ranking dataen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1017-0405&volume=11&issue=2&spage=445&epage=461&date=2001&atitle=Bayesian+analysis+of+wandering+vector+models+for+displaying+ranking+dataen_HK
dc.identifier.emailYu, PLH: plhyu@hkucc.hku.hken_HK
dc.identifier.authorityYu, PLH=rp00835en_HK
dc.description.naturepublished_or_final_versionen_HK
dc.identifier.scopuseid_2-s2.0-0035593915en_HK
dc.identifier.hkuros57083-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0035593915&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume11en_HK
dc.identifier.issue2en_HK
dc.identifier.spage445en_HK
dc.identifier.epage461en_HK
dc.publisher.placeTaiwan, Republic of Chinaen_HK
dc.identifier.scopusauthoridYu, PLH=7403599794en_HK
dc.identifier.scopusauthoridChan, LKY=36907179800en_HK

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