File Download
  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Weighted distance-based models for ranking data using the R package rankdist.

TitleWeighted distance-based models for ranking data using the R package rankdist.
Authors
KeywordsDistance-based models
Kendall distance
Mixtures models
R
Rank aggregation
Issue Date2019
PublisherUniversity of California at Los Angeles, Department of Statistics. The Journal's web site is located at http://www.jstatsoft.org/
Citation
Journal of Statistical Software, 2019, v. 90 n. 5, p. 1-31 How to Cite?
AbstractRankdist is a recently developed R package which implements various distance-based ranking models. These models capture the occurring probability of rankings based on the distances between them. The package provides a framework for fitting and evaluating finite mixture of distance-based models. This paper also presents a new probability model for ranking data based on a new notion of weighted Kendall distance. The new model is flexible and more interpretable than the existing models. We show that the new model has an analytic form of the probability mass function and the maximum likelihood estimates of the model parameters can be obtained efficiently even for ranking involving a large number of objects. © 2019, American Statistical Association. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/275756
ISSN
2019 Impact Factor: 13.642
2015 SCImago Journal Rankings: 2.970
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorQian, Z-
dc.contributor.authorYu, PLH-
dc.date.accessioned2019-09-10T02:49:03Z-
dc.date.available2019-09-10T02:49:03Z-
dc.date.issued2019-
dc.identifier.citationJournal of Statistical Software, 2019, v. 90 n. 5, p. 1-31-
dc.identifier.issn1548-7660-
dc.identifier.urihttp://hdl.handle.net/10722/275756-
dc.description.abstractRankdist is a recently developed R package which implements various distance-based ranking models. These models capture the occurring probability of rankings based on the distances between them. The package provides a framework for fitting and evaluating finite mixture of distance-based models. This paper also presents a new probability model for ranking data based on a new notion of weighted Kendall distance. The new model is flexible and more interpretable than the existing models. We show that the new model has an analytic form of the probability mass function and the maximum likelihood estimates of the model parameters can be obtained efficiently even for ranking involving a large number of objects. © 2019, American Statistical Association. All rights reserved.-
dc.languageeng-
dc.publisherUniversity of California at Los Angeles, Department of Statistics. The Journal's web site is located at http://www.jstatsoft.org/-
dc.relation.ispartofJournal of Statistical Software-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectDistance-based models-
dc.subjectKendall distance-
dc.subjectMixtures models-
dc.subjectR-
dc.subjectRank aggregation-
dc.titleWeighted distance-based models for ranking data using the R package rankdist.-
dc.typeArticle-
dc.identifier.emailYu, PLH: plhyu@hku.hk-
dc.identifier.authorityYu, PLH=rp00835-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.18637/jss.v090.i05-
dc.identifier.scopuseid_2-s2.0-85070519392-
dc.identifier.hkuros303899-
dc.identifier.volume90-
dc.identifier.issue5-
dc.identifier.spage1-
dc.identifier.epage31-
dc.identifier.isiWOS:000477923000001-
dc.publisher.placeUnited States-
dc.identifier.issnl1548-7660-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats