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Article: Mixtures of weighted distance-based models for ranking data with applications in political studies

TitleMixtures of weighted distance-based models for ranking data with applications in political studies
Authors
KeywordsDistance-based models
Mixtures models
Ranking data
Issue Date2012
PublisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/csda
Citation
Computational Statistics And Data Analysis, 2012, v. 56 n. 8, p. 2486-2500 How to Cite?
AbstractAnalysis of ranking data is often required in various fields of study, for example politics, market research and psychology. Over the years, many statistical models for ranking data have been developed. Among them, distance-based ranking models postulate that the probability of observing a ranking of items depends on the distance between the observed ranking and a modal ranking. The closer to the modal ranking, the higher the ranking probability is. However, such a model assumes a homogeneous population, and the single dispersion parameter in the model may not be able to describe the data well. To overcome these limitations, we formulate more flexible models by considering the recently developed weighted distance-based models which can allow different weights for different ranks. The assumption of a homogeneous population can be relaxed by an extension to mixtures of weighted distance-based models. The properties of weighted distance-based models are also discussed. We carry out simulations to test the performance of our parameter estimation and model selection procedures. Finally, we apply the proposed methodology to analyze synthetic ranking datasets and a real world ranking dataset about political goals priority. © 2012 Elsevier B.V. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/152858
ISSN
2015 Impact Factor: 1.179
2015 SCImago Journal Rankings: 1.283
ISI Accession Number ID
Funding AgencyGrant Number
Research Grants Council of the Hong Kong Special Administrative Region, ChinaHKU 7473/05H
Funding Information:

The research of Philip L. H. Yu was supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. HKU 7473/05H). We thank the associate editor and three anonymous referees for their helpful suggestions for improving this article.

References
Grants

 

DC FieldValueLanguage
dc.contributor.authorLee, PHen_HK
dc.contributor.authorYu, PLHen_HK
dc.date.accessioned2012-07-16T09:50:48Z-
dc.date.available2012-07-16T09:50:48Z-
dc.date.issued2012en_HK
dc.identifier.citationComputational Statistics And Data Analysis, 2012, v. 56 n. 8, p. 2486-2500en_HK
dc.identifier.issn0167-9473en_HK
dc.identifier.urihttp://hdl.handle.net/10722/152858-
dc.description.abstractAnalysis of ranking data is often required in various fields of study, for example politics, market research and psychology. Over the years, many statistical models for ranking data have been developed. Among them, distance-based ranking models postulate that the probability of observing a ranking of items depends on the distance between the observed ranking and a modal ranking. The closer to the modal ranking, the higher the ranking probability is. However, such a model assumes a homogeneous population, and the single dispersion parameter in the model may not be able to describe the data well. To overcome these limitations, we formulate more flexible models by considering the recently developed weighted distance-based models which can allow different weights for different ranks. The assumption of a homogeneous population can be relaxed by an extension to mixtures of weighted distance-based models. The properties of weighted distance-based models are also discussed. We carry out simulations to test the performance of our parameter estimation and model selection procedures. Finally, we apply the proposed methodology to analyze synthetic ranking datasets and a real world ranking dataset about political goals priority. © 2012 Elsevier B.V. All rights reserved.en_HK
dc.languageengen_US
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.subjectDistance-based modelsen_HK
dc.subjectMixtures modelsen_HK
dc.subjectRanking dataen_HK
dc.titleMixtures of weighted distance-based models for ranking data with applications in political studiesen_HK
dc.typeArticleen_HK
dc.identifier.emailYu, PLH: plhyu@hkucc.hku.hken_HK
dc.identifier.authorityYu, PLH=rp00835en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.csda.2012.02.002en_HK
dc.identifier.scopuseid_2-s2.0-84859100519en_HK
dc.identifier.hkuros201391en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-84859100519&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume56en_HK
dc.identifier.issue8en_HK
dc.identifier.spage2486en_HK
dc.identifier.epage2500en_HK
dc.identifier.isiWOS:000303035000010-
dc.publisher.placeNetherlandsen_HK
dc.relation.projectModeling of ranking data: a decision tree approach-
dc.identifier.scopusauthoridLee, PH=35362305200en_HK
dc.identifier.scopusauthoridYu, PLH=7403599794en_HK
dc.identifier.citeulike10361477-

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