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Article: Weighted distance-based models for ranking data using the R package rankdist.
Title | Weighted distance-based models for ranking data using the R package rankdist. |
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Authors | |
Keywords | Distance-based models Kendall distance Mixtures models R Rank aggregation |
Issue Date | 2019 |
Publisher | University 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? |
Abstract | Rankdist 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 Identifier | http://hdl.handle.net/10722/275756 |
ISSN | 2023 Impact Factor: 5.4 2023 SCImago Journal Rankings: 2.709 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Qian, Z | - |
dc.contributor.author | Yu, PLH | - |
dc.date.accessioned | 2019-09-10T02:49:03Z | - |
dc.date.available | 2019-09-10T02:49:03Z | - |
dc.date.issued | 2019 | - |
dc.identifier.citation | Journal of Statistical Software, 2019, v. 90 n. 5, p. 1-31 | - |
dc.identifier.issn | 1548-7660 | - |
dc.identifier.uri | http://hdl.handle.net/10722/275756 | - |
dc.description.abstract | Rankdist 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.language | eng | - |
dc.publisher | University of California at Los Angeles, Department of Statistics. The Journal's web site is located at http://www.jstatsoft.org/ | - |
dc.relation.ispartof | Journal of Statistical Software | - |
dc.rights | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. | - |
dc.subject | Distance-based models | - |
dc.subject | Kendall distance | - |
dc.subject | Mixtures models | - |
dc.subject | R | - |
dc.subject | Rank aggregation | - |
dc.title | Weighted distance-based models for ranking data using the R package rankdist. | - |
dc.type | Article | - |
dc.identifier.email | Yu, PLH: plhyu@hku.hk | - |
dc.identifier.authority | Yu, PLH=rp00835 | - |
dc.description.nature | published_or_final_version | - |
dc.identifier.doi | 10.18637/jss.v090.i05 | - |
dc.identifier.scopus | eid_2-s2.0-85070519392 | - |
dc.identifier.hkuros | 303899 | - |
dc.identifier.volume | 90 | - |
dc.identifier.issue | 5 | - |
dc.identifier.spage | 1 | - |
dc.identifier.epage | 31 | - |
dc.identifier.isi | WOS:000477923000001 | - |
dc.publisher.place | United States | - |
dc.identifier.issnl | 1548-7660 | - |