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Conference Paper: Mixtures of weighted distance-based models for ranking data

TitleMixtures of weighted distance-based models for ranking data
Authors
KeywordsRanking data
Distance-based model
Mixture model
Issue Date2010
PublisherSpringer-Verlag.
Citation
The 19th International Conference on Computational Statistics (COMPSTAT' 2010), Paris, France, 22-27 August 2010. In Proceedings of COMPSTAT, 2010, pt. 16, p. 517-524 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 the single dispersion parameter may not be able to describe the data very well. To overcome the limitations, we consider new weighted distance measures which allow different weights for different ranks in formulating more flexible distance-based models. The mixtures of weighted distance-based models are also studied for analyzing heterogeneous data. Simulations results will be included, and we will apply the proposed methodology to analyze a real world ranking dataset.
Persistent Identifierhttp://hdl.handle.net/10722/127199
ISBN

 

DC FieldValueLanguage
dc.contributor.authorLee, PHen_HK
dc.contributor.authorYu, PLHen_HK
dc.date.accessioned2010-10-31T13:11:51Z-
dc.date.available2010-10-31T13:11:51Z-
dc.date.issued2010en_HK
dc.identifier.citationThe 19th International Conference on Computational Statistics (COMPSTAT' 2010), Paris, France, 22-27 August 2010. In Proceedings of COMPSTAT, 2010, pt. 16, p. 517-524en_HK
dc.identifier.isbn9783790826036en_HK
dc.identifier.urihttp://hdl.handle.net/10722/127199-
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 the single dispersion parameter may not be able to describe the data very well. To overcome the limitations, we consider new weighted distance measures which allow different weights for different ranks in formulating more flexible distance-based models. The mixtures of weighted distance-based models are also studied for analyzing heterogeneous data. Simulations results will be included, and we will apply the proposed methodology to analyze a real world ranking dataset.-
dc.languageengen_HK
dc.publisherSpringer-Verlag.en_HK
dc.relation.ispartofProceedings of COMPSTAT' 2010en_HK
dc.rightsThe original publication is available at www.springerlink.com-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectRanking data-
dc.subjectDistance-based model-
dc.subjectMixture model-
dc.titleMixtures of weighted distance-based models for ranking dataen_HK
dc.typeConference_Paperen_HK
dc.identifier.emailLee, PH: honglee@hku.hken_HK
dc.identifier.emailYu, PLH: plhyu@hku.hken_HK
dc.identifier.authorityYu, PLH=rp00835en_HK
dc.identifier.doi10.1007/978-3-7908-2604-3_52-
dc.identifier.hkuros178995en_HK
dc.identifier.spage517en_HK
dc.identifier.epage524en_HK
dc.publisher.placeGermany-
dc.description.otherThe 19th International Conference on Computational Statistics (COMPSTAT' 2010), Paris, France, 22-27 August 2010. In Proceedings of COMPSTAT, 2010, pt. 16, p. 517-524-

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