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- Publisher Website: 10.1007/s11222-018-9811-9
- Scopus: eid_2-s2.0-85045069803
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Article: Rank aggregation using latent-scale distance-based models
Title | Rank aggregation using latent-scale distance-based models |
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Authors | |
Keywords | Ranking data Latent-scale distance-based model Rank aggregration Incomplete ranking |
Issue Date | 2018 |
Publisher | Springer New York LLC. The Journal's web site is located at http://springerlink.metapress.com/openurl.asp?genre=journal&issn=0960-3174 |
Citation | Statistics and Computing, 2018, p. 1-15 How to Cite? |
Abstract | Rank aggregation aims at combining rankings of a set of items assigned by a sample of rankers to generate a consensus ranking. A typical solution is to adopt a distance-based approach to minimize the sum of the distances to the observed rankings. However, this simple sum may not be appropriate when the quality of rankers varies. This happens when rankers with different backgrounds may have different cognitive levels of examining the items. In this paper, we develop a new distance-based model by allowing different weights for different rankers. Under this model, the weight associated with a ranker is used to measure his/her cognitive level of ranking of the items, and these weights are unobserved and exponentially distributed. Maximum likelihood method is used for model estimation. Extensions to the cases of incomplete rankings and mixture modeling are also discussed. Empirical applications demonstrate that the proposed model produces better rank aggregation than those generated by Borda and the unweighted distance-based models. |
Persistent Identifier | http://hdl.handle.net/10722/261388 |
ISSN | 2023 Impact Factor: 1.6 2023 SCImago Journal Rankings: 0.923 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Yu, PLH | - |
dc.contributor.author | Xu, H | - |
dc.date.accessioned | 2018-09-14T08:57:21Z | - |
dc.date.available | 2018-09-14T08:57:21Z | - |
dc.date.issued | 2018 | - |
dc.identifier.citation | Statistics and Computing, 2018, p. 1-15 | - |
dc.identifier.issn | 0960-3174 | - |
dc.identifier.uri | http://hdl.handle.net/10722/261388 | - |
dc.description.abstract | Rank aggregation aims at combining rankings of a set of items assigned by a sample of rankers to generate a consensus ranking. A typical solution is to adopt a distance-based approach to minimize the sum of the distances to the observed rankings. However, this simple sum may not be appropriate when the quality of rankers varies. This happens when rankers with different backgrounds may have different cognitive levels of examining the items. In this paper, we develop a new distance-based model by allowing different weights for different rankers. Under this model, the weight associated with a ranker is used to measure his/her cognitive level of ranking of the items, and these weights are unobserved and exponentially distributed. Maximum likelihood method is used for model estimation. Extensions to the cases of incomplete rankings and mixture modeling are also discussed. Empirical applications demonstrate that the proposed model produces better rank aggregation than those generated by Borda and the unweighted distance-based models. | - |
dc.language | eng | - |
dc.publisher | Springer New York LLC. The Journal's web site is located at http://springerlink.metapress.com/openurl.asp?genre=journal&issn=0960-3174 | - |
dc.relation.ispartof | Statistics and Computing | - |
dc.rights | The final publication is available at Springer via http://dx.doi.org/[insert DOI] | - |
dc.subject | Ranking data | - |
dc.subject | Latent-scale distance-based model | - |
dc.subject | Rank aggregration | - |
dc.subject | Incomplete ranking | - |
dc.title | Rank aggregation using latent-scale distance-based models | - |
dc.type | Article | - |
dc.identifier.email | Yu, PLH: plhyu@hku.hk | - |
dc.identifier.authority | Yu, PLH=rp00835 | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1007/s11222-018-9811-9 | - |
dc.identifier.scopus | eid_2-s2.0-85045069803 | - |
dc.identifier.hkuros | 290955 | - |
dc.identifier.spage | 1 | - |
dc.identifier.epage | 15 | - |
dc.identifier.isi | WOS:000459016300009 | - |
dc.publisher.place | United States | - |
dc.identifier.issnl | 0960-3174 | - |