Change Point Detection in Pairwise Comparison Data with Covariates
<p>This paper introduces the novel piecewise stationary covariate-assisted ranking estimation (PS-CARE) model for analyzing time-evolving pairwise comparison data, enhancing item ranking accuracy through the integration of covariate information. By partitioning the data into distinct, stationa...
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| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | |
| منشور في: |
2025
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| الموضوعات: | |
| الوسوم: |
إضافة وسم
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| _version_ | 1852015235797352448 |
|---|---|
| author | Yi Han (194901) |
| author2 | Thomas C. M. Lee (7372220) |
| author2_role | author |
| author_facet | Yi Han (194901) Thomas C. M. Lee (7372220) |
| author_role | author |
| dc.creator.none.fl_str_mv | Yi Han (194901) Thomas C. M. Lee (7372220) |
| dc.date.none.fl_str_mv | 2025-10-31T20:00:07Z |
| dc.identifier.none.fl_str_mv | 10.6084/m9.figshare.30505906.v1 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/Change_Point_Detection_in_Pairwise_Comparison_Data_with_Covariates/30505906 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biochemistry Science Policy Infectious Diseases Biological Sciences not elsewhere classified Information Systems not elsewhere classified Bradley-Terry-Luce model covariate-assisted ranking estimation (CARE) model minimum description length (MDL) pruned exact linear time (PELT) algorithm ranking problem |
| dc.title.none.fl_str_mv | Change Point Detection in Pairwise Comparison Data with Covariates |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | <p>This paper introduces the novel piecewise stationary covariate-assisted ranking estimation (PS-CARE) model for analyzing time-evolving pairwise comparison data, enhancing item ranking accuracy through the integration of covariate information. By partitioning the data into distinct, stationary segments, the PS-CARE model adeptly detects temporal shifts in item rankings, known as change points, whose number and positions are initially unknown. Leveraging the minimum description length (MDL) principle, this paper establishes a statistically consistent model selection criterion to estimate these unknowns. The practical optimization of this MDL criterion is done with the pruned exact linear time (PELT) algorithm. Empirical evaluations reveal the method’s promising performance in accurately locating change points across various simulated scenarios. An application to an NBA dataset yielded meaningful insights that aligned with significant historical events, highlighting the method’s practical utility and the MDL criterion’s effectiveness in capturing temporal ranking changes. To the best of the authors’ knowledge, this research pioneers change point detection in pairwise comparison data with covariate information, representing a significant leap forward in the field of dynamic ranking analysis.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_c53c8e2fda71cf1da031cd557e155ddc |
| identifier_str_mv | 10.6084/m9.figshare.30505906.v1 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/30505906 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Change Point Detection in Pairwise Comparison Data with CovariatesYi Han (194901)Thomas C. M. Lee (7372220)BiochemistryScience PolicyInfectious DiseasesBiological Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedBradley-Terry-Luce modelcovariate-assisted ranking estimation (CARE) modelminimum description length (MDL)pruned exact linear time (PELT) algorithmranking problem<p>This paper introduces the novel piecewise stationary covariate-assisted ranking estimation (PS-CARE) model for analyzing time-evolving pairwise comparison data, enhancing item ranking accuracy through the integration of covariate information. By partitioning the data into distinct, stationary segments, the PS-CARE model adeptly detects temporal shifts in item rankings, known as change points, whose number and positions are initially unknown. Leveraging the minimum description length (MDL) principle, this paper establishes a statistically consistent model selection criterion to estimate these unknowns. The practical optimization of this MDL criterion is done with the pruned exact linear time (PELT) algorithm. Empirical evaluations reveal the method’s promising performance in accurately locating change points across various simulated scenarios. An application to an NBA dataset yielded meaningful insights that aligned with significant historical events, highlighting the method’s practical utility and the MDL criterion’s effectiveness in capturing temporal ranking changes. To the best of the authors’ knowledge, this research pioneers change point detection in pairwise comparison data with covariate information, representing a significant leap forward in the field of dynamic ranking analysis.</p>2025-10-31T20:00:07ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.6084/m9.figshare.30505906.v1https://figshare.com/articles/dataset/Change_Point_Detection_in_Pairwise_Comparison_Data_with_Covariates/30505906CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/305059062025-10-31T20:00:07Z |
| spellingShingle | Change Point Detection in Pairwise Comparison Data with Covariates Yi Han (194901) Biochemistry Science Policy Infectious Diseases Biological Sciences not elsewhere classified Information Systems not elsewhere classified Bradley-Terry-Luce model covariate-assisted ranking estimation (CARE) model minimum description length (MDL) pruned exact linear time (PELT) algorithm ranking problem |
| status_str | publishedVersion |
| title | Change Point Detection in Pairwise Comparison Data with Covariates |
| title_full | Change Point Detection in Pairwise Comparison Data with Covariates |
| title_fullStr | Change Point Detection in Pairwise Comparison Data with Covariates |
| title_full_unstemmed | Change Point Detection in Pairwise Comparison Data with Covariates |
| title_short | Change Point Detection in Pairwise Comparison Data with Covariates |
| title_sort | Change Point Detection in Pairwise Comparison Data with Covariates |
| topic | Biochemistry Science Policy Infectious Diseases Biological Sciences not elsewhere classified Information Systems not elsewhere classified Bradley-Terry-Luce model covariate-assisted ranking estimation (CARE) model minimum description length (MDL) pruned exact linear time (PELT) algorithm ranking problem |