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- Publisher Website: 10.1016/j.csda.2017.12.004
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Article: Angle-based models for ranking data
Title | Angle-based models for ranking data |
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Authors | |
Keywords | Bayesian variational inference Incomplete ranking Ranking data |
Issue Date | 2018 |
Publisher | Elsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/csda |
Citation | Computational Statistics & Data Analysis, 2018, v. 121, p. 113-136 How to Cite? |
Abstract | A new class of general exponential ranking models is introduced which we label angle-based models for ranking data. A consensus score vector is assumed, which assigns scores to a set of items, where the scores reflect a consensus view of the relative preference of the items. The probability of observing a ranking is modeled to be proportional to its cosine of the angle from the consensus vector. Bayesian variational inference is employed to determine the corresponding predictive density. It can be seen from simulation experiments that the Bayesian variational inference approach not only has great computational advantage compared to the traditional MCMC, but also avoids the problem of overfitting inherent when using maximum likelihood methods. The model also works when a large number of items are ranked which is usually an NP-hard problem to find the estimate of parameters for other classes of ranking models. Model extensions to incomplete rankings and mixture models are also developed. Real data applications demonstrate that the model and extensions can handle different tasks for the analysis of ranking data. |
Persistent Identifier | http://hdl.handle.net/10722/260589 |
ISSN | 2023 Impact Factor: 1.5 2023 SCImago Journal Rankings: 1.008 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | XU, H | - |
dc.contributor.author | Alvo, M. | - |
dc.contributor.author | Yu, PLH | - |
dc.date.accessioned | 2018-09-14T08:44:09Z | - |
dc.date.available | 2018-09-14T08:44:09Z | - |
dc.date.issued | 2018 | - |
dc.identifier.citation | Computational Statistics & Data Analysis, 2018, v. 121, p. 113-136 | - |
dc.identifier.issn | 0167-9473 | - |
dc.identifier.uri | http://hdl.handle.net/10722/260589 | - |
dc.description.abstract | A new class of general exponential ranking models is introduced which we label angle-based models for ranking data. A consensus score vector is assumed, which assigns scores to a set of items, where the scores reflect a consensus view of the relative preference of the items. The probability of observing a ranking is modeled to be proportional to its cosine of the angle from the consensus vector. Bayesian variational inference is employed to determine the corresponding predictive density. It can be seen from simulation experiments that the Bayesian variational inference approach not only has great computational advantage compared to the traditional MCMC, but also avoids the problem of overfitting inherent when using maximum likelihood methods. The model also works when a large number of items are ranked which is usually an NP-hard problem to find the estimate of parameters for other classes of ranking models. Model extensions to incomplete rankings and mixture models are also developed. Real data applications demonstrate that the model and extensions can handle different tasks for the analysis of ranking data. | - |
dc.language | eng | - |
dc.publisher | Elsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/csda | - |
dc.relation.ispartof | Computational Statistics & Data Analysis | - |
dc.subject | Bayesian variational inference | - |
dc.subject | Incomplete ranking | - |
dc.subject | Ranking data | - |
dc.title | Angle-based models for ranking data | - |
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 | 10.1016/j.csda.2017.12.004 | - |
dc.identifier.scopus | eid_2-s2.0-85040118085 | - |
dc.identifier.hkuros | 290939 | - |
dc.identifier.volume | 121 | - |
dc.identifier.spage | 113 | - |
dc.identifier.epage | 136 | - |
dc.identifier.isi | WOS:000429083600008 | - |
dc.publisher.place | Netherlands | - |
dc.identifier.issnl | 0167-9473 | - |