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Article: Distance-based tree models for ranking data

TitleDistance-based tree models for ranking data
Authors
KeywordsDecision tree
Distance-based model
Ranking data
Issue Date2010
PublisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/csda
Citation
Computational Statistics And Data Analysis, 2010, v. 54 n. 6, p. 1672-1682 How to Cite?
AbstractRanking data has applications in different fields of studies, like marketing, psychology and politics. Over the years, many models for ranking data have been developed. Among them, distance-based ranking models, which originate from the classical rank correlations, 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 basically assumes a homogeneous population and does not incorporate the presence of covariates. To overcome these limitations, we combine the strength of a tree model and the existing distance-based models to build a model that can handle more complexity and improve prediction accuracy. We will introduce a recursive partitioning algorithm for building a tree model with a distance-based ranking model fitted at each leaf. We will also consider new weighted distance measures which allow different weights for different ranks in formulating more flexible distance-based tree models. Finally, we will apply the proposed methodology to analyze a ranking dataset of Inglehart's items collected in the 1999 European Values Studies. © 2010 Elsevier B.V. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/129374
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 authors would like to thank the anonymous reviewers for their helpful comments which greatly improved this paper. 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).

References
Grants

 

DC FieldValueLanguage
dc.contributor.authorLee, PHen_HK
dc.contributor.authorYu, PLHen_HK
dc.date.accessioned2010-12-23T08:36:25Z-
dc.date.available2010-12-23T08:36:25Z-
dc.date.issued2010en_HK
dc.identifier.citationComputational Statistics And Data Analysis, 2010, v. 54 n. 6, p. 1672-1682en_HK
dc.identifier.issn0167-9473en_HK
dc.identifier.urihttp://hdl.handle.net/10722/129374-
dc.description.abstractRanking data has applications in different fields of studies, like marketing, psychology and politics. Over the years, many models for ranking data have been developed. Among them, distance-based ranking models, which originate from the classical rank correlations, 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 basically assumes a homogeneous population and does not incorporate the presence of covariates. To overcome these limitations, we combine the strength of a tree model and the existing distance-based models to build a model that can handle more complexity and improve prediction accuracy. We will introduce a recursive partitioning algorithm for building a tree model with a distance-based ranking model fitted at each leaf. We will also consider new weighted distance measures which allow different weights for different ranks in formulating more flexible distance-based tree models. Finally, we will apply the proposed methodology to analyze a ranking dataset of Inglehart's items collected in the 1999 European Values Studies. © 2010 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.subjectDecision treeen_HK
dc.subjectDistance-based modelen_HK
dc.subjectRanking dataen_HK
dc.titleDistance-based tree models for ranking dataen_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.2010.01.027en_HK
dc.identifier.scopuseid_2-s2.0-77249161117en_HK
dc.identifier.hkuros178324en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-77249161117&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume54en_HK
dc.identifier.issue6en_HK
dc.identifier.spage1672en_HK
dc.identifier.epage1682en_HK
dc.identifier.isiWOS:000276534500024-
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.citeulike6639053-

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