{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T18:04:17Z","timestamp":1790100257391,"version":"4.0.1"},"reference-count":36,"publisher":"Informa UK Limited","license":[{"start":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T00:00:00Z","timestamp":1790035200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/doi.org\/10.1080\/policies-text-data-mining"}],"content-domain":{"domain":["www.tandfonline.com"],"crossmark-restriction":true},"short-container-title":["Journal of the American Statistical Association"],"DOI":"10.1080\/01621459.2026.2735065","type":"journal-article","created":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T17:21:15Z","timestamp":1790097675000},"page":"1-91","update-policy":"https:\/\/doi.org\/10.1080\/tandf_crossmark_01","source":"Crossref","is-referenced-by-count":0,"title":["Entity Ranking with Covariates and Sparse Intrinsic Scores"],"prefix":"10.1080","author":[{"given":"Jianqing","family":"Fan","sequence":"first","affiliation":[{"name":"Frederick L. Moore \u201918 Professor of Finance and Professor of Operations Research and Financial Engineering at Princeton University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jikai","family":"Hou","sequence":"additional","affiliation":[{"name":"Graduate Student at the Operations Research and Financial Engineering at Princeton University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6818-4083","authenticated-orcid":false,"given":"Mengxin","family":"Yu","sequence":"additional","affiliation":[{"name":"Assistant Professor at the Department of Statistics and Data Science, WUSTL"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"301","published-online":{"date-parts":[[2026,9,22]]},"reference":[{"key":"e_1_3_1_2_1","doi-asserted-by":"crossref","unstructured":"Baltrunas L. Makcinskas T. and Ricci F. (2010). Group recommendations with rank aggregation and collaborative filtering. In Proceedings of the fourth ACM conference on Recommender systems.","DOI":"10.1145\/1864708.1864733"},{"key":"e_1_3_1_3_1","doi-asserted-by":"crossref","unstructured":"Bradley R. A. and Terry M. E. (1952). Rank analysis of incomplete block designs: I. the method of paired comparisons. Biometrika 39 324\u2013345.","DOI":"10.1093\/biomet\/39.3-4.324"},{"key":"e_1_3_1_4_1","doi-asserted-by":"crossref","unstructured":"Caron F. Teh Y. W. and Murphy T. B. (2014). Bayesian nonparametric plackett\u2013luce models for the analysis of preferences for college degree programmes. Annals of Applied Statistics 8 1145\u20131181.","DOI":"10.1214\/14-AOAS717"},{"key":"e_1_3_1_5_1","doi-asserted-by":"crossref","unstructured":"Chau S. L. Cucuringu M. and Sejdinovic D. (2023). Spectral ranking with covariates. In Machine Learning and Knowledge Discovery in Databases: European Conference ECML PKDD 2022 Grenoble France September 19\u201323 2022 Proceedings Part V. Springer.","DOI":"10.1007\/978-3-031-26419-1_5"},{"key":"e_1_3_1_6_1","doi-asserted-by":"crossref","unstructured":"Chen P. Gao C. and Zhang A. Y. (2022a). Optimal full ranking from pairwise comparisons. The Annals of Statistics 50 1775\u20131805.","DOI":"10.1214\/22-AOS2175"},{"key":"e_1_3_1_7_1","doi-asserted-by":"crossref","unstructured":"Chen P. Gao C. and Zhang A. Y. (2022b). Partial recovery for top-k ranking: Optimality of mle and suboptimality of the spectral method. The Annals of Statistics 50 1618\u20131652.","DOI":"10.1214\/21-AOS2166"},{"key":"e_1_3_1_8_1","doi-asserted-by":"crossref","unstructured":"Chen Y. Fan J. Ma C. and Wang K. (2019). Spectral method and regularized mle are both optimal for top-k ranking. Annals of statistics 47 2204.","DOI":"10.1214\/18-AOS1745"},{"key":"e_1_3_1_9_1","unstructured":"Chen Y. and Suh C. (2015). Spectral mle: Top-k rank aggregation from pairwise comparisons. In International Conference on Machine Learning. PMLR."},{"key":"e_1_3_1_10_1","doi-asserted-by":"crossref","unstructured":"Chernozhuokov V. Chetverikov D. Kato K. and Koike Y. (2022). Improved central limit theorem and bootstrap approximations in high dimensions. The Annals of Statistics 50 2562\u20132586.","DOI":"10.1214\/22-AOS2193"},{"key":"e_1_3_1_11_1","doi-asserted-by":"crossref","unstructured":"Dong P. Han R. Jiang B. and Xu Y. (2026). Statistical ranking with dynamic covariates. Journal of the Royal Statistical Society Series B 88 221\u2013238.","DOI":"10.1093\/jrsssb\/qkaf048"},{"key":"e_1_3_1_12_1","doi-asserted-by":"crossref","unstructured":"Dwork C. Kumar R. Naor M. and Sivakumar D. (2001). Rank aggregation methods for the web. In Proceedings of the 10th international conference on World Wide Web.","DOI":"10.1145\/371920.372165"},{"key":"e_1_3_1_13_1","unstructured":"Fan J. Hou J. and Yu M. (2024). Uncertainty quantification of mle for entity ranking with covariates. Journal of Machine Learning Research 25 1\u201383."},{"key":"e_1_3_1_14_1","doi-asserted-by":"crossref","unstructured":"Fan J. Lou Z. Wang W. and Yu M. (2025). Ranking inferences based on the top choice of multiway comparisons. Journal of the American Statistical Association 120 237\u2013250.","DOI":"10.1080\/01621459.2024.2316364"},{"key":"e_1_3_1_15_1","doi-asserted-by":"crossref","unstructured":"Fan J. Lou Z. Wang W. and Yu M. (2026). Spectral ranking inferences based on general multiway comparisons. Operations Research 74 161\u2013180.","DOI":"10.1287\/opre.2023.0439"},{"key":"e_1_3_1_16_1","unstructured":"Finch H. (2022). An introduction to the analysis of ranked response data. Practical Assessment Research and Evaluation 27 7."},{"key":"e_1_3_1_17_1","doi-asserted-by":"crossref","unstructured":"Gao C. Shen Y. and Zhang A. Y. (2023). Uncertainty quantification in the bradley\u2013terry\u2013luce model. Information and Inference: A Journal of the IMA 12 1073\u20131140.","DOI":"10.1093\/imaiai\/iaac032"},{"key":"e_1_3_1_18_1","doi-asserted-by":"crossref","unstructured":"Guo Y. Tian P. Kalpathy-Cramer J. Ostmo S. Campbell J. P. Chiang M. F. Erdogmus D. Dy J. G. and Ioannidis S. (2018). Experimental design under the bradley-terry model. In IJCAI.","DOI":"10.24963\/ijcai.2018\/304"},{"key":"e_1_3_1_19_1","unstructured":"Han R. and Xu Y. (2023). A unified analysis of likelihood-based estimators in the plackett\u2013luce model. arXiv preprint arXiv:2306.02821."},{"key":"e_1_3_1_20_1","doi-asserted-by":"crossref","unstructured":"Han R. Ye R. Tan C. and Chen K. (2020). Asymptotic theory of sparse bradley\u2013terry model. The Annals of Applied Probability 30 2491\u20132515.","DOI":"10.1214\/20-AAP1564"},{"key":"e_1_3_1_21_1","doi-asserted-by":"crossref","unstructured":"Ji P. Jin J. Ke Z. T. and Li W. (2022). Co-citation and co-authorship networks of statisticians. Journal of Business & Economic Statistics 40 469\u2013485.","DOI":"10.1080\/07350015.2021.1978469"},{"key":"e_1_3_1_22_1","doi-asserted-by":"crossref","unstructured":"Li H. Simchi-Levi D. Wu M. X. and Zhu W. (2019). Estimating and exploiting the impact of photo layout: A structural approach. Available at SSRN 3470877.","DOI":"10.2139\/ssrn.3470877"},{"key":"e_1_3_1_23_1","doi-asserted-by":"crossref","unstructured":"Li X. Yi D. and Liu J. S. (2022). Bayesian analysis of rank data with covariates and heterogeneous rankers. Statistical Science 37 1\u201323.","DOI":"10.1214\/20-STS818"},{"key":"e_1_3_1_24_1","doi-asserted-by":"crossref","unstructured":"Liu Y. Fang E. X. and Lu J. (2022). Lagrangian inference for ranking problems. Operations Research.","DOI":"10.1287\/opre.2022.2313"},{"key":"e_1_3_1_25_1","doi-asserted-by":"crossref","unstructured":"Luce R. D. (2005). Individual choice behavior: A theoretical analysis. Courier Corporation.","DOI":"10.1037\/14396-000"},{"key":"e_1_3_1_26_1","unstructured":"Luce R. D. (2012). Individual choice behavior: A theoretical analysis. Courier Corporation."},{"key":"e_1_3_1_27_1","unstructured":"Massey K. (1997). Statistical models applied to the rating of sports teams. Bluefield College."},{"key":"e_1_3_1_28_1","unstructured":"Negahban S. Oh S. and Shah D. (2012). Iterative ranking from pair-wise comparisons. Advances in neural information processing systems 25."},{"key":"e_1_3_1_29_1","doi-asserted-by":"crossref","unstructured":"Ouyang L. Wu J. Jiang X. Almeida D. Wainwright C. Mishkin P. Zhang C. Agarwal S. Slama K. Ray A. et\u00a0al. (2022). Training language models to follow instructions with human feedback. Advances in neural information processing systems 35 27730\u201327744.","DOI":"10.52202\/068431-2011"},{"key":"e_1_3_1_30_1","doi-asserted-by":"crossref","unstructured":"Plackett R. L. (1975). The analysis of permutations. Journal of the Royal Statistical Society: Series C (Applied Statistics) 24 193\u2013202.","DOI":"10.2307\/2346567"},{"key":"e_1_3_1_31_1","doi-asserted-by":"crossref","unstructured":"Rusmevichientong P. Shen Z.-J. M. and Shmoys D. B. (2010). Dynamic assortment optimization with a multinomial logit choice model and capacity constraint. Operations research 58 1666\u20131680.","DOI":"10.1287\/opre.1100.0866"},{"key":"e_1_3_1_32_1","doi-asserted-by":"crossref","unstructured":"Sch\u00e4fer D. and H\u00fcllermeier E. (2018). Dyad ranking using plackett\u2013luce models based on joint feature representations. Machine Learning 107 903\u2013941.","DOI":"10.1007\/s10994-017-5694-9"},{"key":"e_1_3_1_33_1","doi-asserted-by":"crossref","unstructured":"Simons G. and Yao Y.-C. (1999). Asymptotics when the number of parameters tends to infinity in the bradley-terry model for paired comparisons. The Annals of Statistics 27 1041\u20131060.","DOI":"10.1214\/aos\/1018031267"},{"key":"e_1_3_1_34_1","doi-asserted-by":"crossref","unstructured":"Stigler S. M. (1994). Citation patterns in the journals of statistics and probability. Statistical Science 94\u2013108.","DOI":"10.1214\/ss\/1177010655"},{"key":"e_1_3_1_35_1","doi-asserted-by":"crossref","unstructured":"Talluri K. and Van Ryzin G. (2004). Revenue management under a general discrete choice model of consumer behavior. Management Science 50 15\u201333.","DOI":"10.1287\/mnsc.1030.0147"},{"key":"e_1_3_1_36_1","doi-asserted-by":"crossref","unstructured":"Turner H. and Firth D. (2012). Bradley-terry models in r: the bradleyterry2 package. Journal of Statistical Software 48 1\u201321.","DOI":"10.18637\/jss.v048.i09"},{"key":"e_1_3_1_37_1","doi-asserted-by":"crossref","unstructured":"Zhao Z. Liu A. and Xia L. (2022). Learning mixtures of random utility models with features from incomplete preferences. arXiv preprint arXiv:2006.03869.","DOI":"10.24963\/ijcai.2022\/525"}],"container-title":["Journal of the American Statistical Association"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.tandfonline.com\/doi\/pdf\/10.1080\/01621459.2026.2735065","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.tandfonline.com\/doi\/full-xml\/10.1080\/01621459.2026.2735065","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.tandfonline.com\/doi\/pdf\/10.1080\/01621459.2026.2735065","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T17:21:26Z","timestamp":1790097686000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.tandfonline.com\/doi\/full\/10.1080\/01621459.2026.2735065"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,22]]},"references-count":36,"alternative-id":["10.1080\/01621459.2026.2735065"],"URL":"https:\/\/doi.org\/10.1080\/01621459.2026.2735065","relation":{},"ISSN":["0162-1459","1537-274X"],"issn-type":[{"value":"0162-1459","type":"print"},{"value":"1537-274X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,22]]},"assertion":[{"value":"The publishing and review policy for this title is described in its Aims & Scope.","order":1,"name":"peerreview_statement","label":"Peer Review Statement"},{"value":"http:\/\/www.tandfonline.com\/action\/journalInformation?show=aimsScope&journalCode=uasa20","URL":"http:\/\/www.tandfonline.com\/action\/journalInformation?show=aimsScope&journalCode=uasa20","order":2,"name":"aims_and_scope_url","label":"Aim & Scope"},{"value":"2024-07-16","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-08-22","order":1,"name":"revised","label":"Revised","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-09-11","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-09-22","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}