{"id":"https://openalex.org/W4402704323","doi":"https://doi.org/10.1016/j.csda.2026.108445","title":"Non-parametric Bayesian inference for partial orders with ties from rank data observed with Mallows noise","display_name":"Non-parametric Bayesian inference for partial orders with ties from rank data observed with Mallows noise","publication_year":2026,"publication_date":"2026-06-25","ids":{"openalex":"https://openalex.org/W4402704323","doi":"https://doi.org/10.1016/j.csda.2026.108445"},"language":"en","primary_location":{"id":"doi:10.1016/j.csda.2026.108445","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.csda.2026.108445","pdf_url":null,"source":{"id":"https://openalex.org/S132362803","display_name":"Computational Statistics & Data Analysis","issn_l":"0167-9473","issn":["0167-9473","1872-7352"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computational Statistics & Data Analysis","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://doi.org/10.1016/j.csda.2026.108445","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5108277508","display_name":"Jiang","orcid":null},"institutions":[{"id":"https://openalex.org/I40120149","display_name":"University of Oxford","ror":"https://ror.org/052gg0110","country_code":"GB","type":"education","lineage":["https://openalex.org/I40120149"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Chuxuan (Jessie) Jiang","raw_affiliation_strings":["Department of Statistics, University of Oxford, 24-29 St Giles, Oxford, OX1 3LB, UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics, University of Oxford, 24-29 St Giles, Oxford, OX1 3LB, UK","institution_ids":["https://openalex.org/I40120149"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5009428718","display_name":"Geoff K. Nicholls","orcid":"https://orcid.org/0000-0002-1595-9041"},"institutions":[{"id":"https://openalex.org/I40120149","display_name":"University of Oxford","ror":"https://ror.org/052gg0110","country_code":"GB","type":"education","lineage":["https://openalex.org/I40120149"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Geoff K. Nicholls","raw_affiliation_strings":["Department of Statistics, University of Oxford, 24-29 St Giles, Oxford, OX1 3LB, UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics, University of Oxford, 24-29 St Giles, Oxford, OX1 3LB, UK","institution_ids":["https://openalex.org/I40120149"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5009428718"],"corresponding_institution_ids":["https://openalex.org/I40120149"],"apc_list":{"value":3340,"currency":"USD","value_usd":3340},"apc_paid":{"value":3340,"currency":"USD","value_usd":3340},"fwci":0.0,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.01054384,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":"224","issue":null,"first_page":"108445","last_page":"108445"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10968","display_name":"Statistical Distribution Estimation and Applications","score":0.9210000038146973,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10968","display_name":"Statistical Distribution Estimation and Applications","score":0.9210000038146973,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10711","display_name":"Target Tracking and Data Fusion in Sensor Networks","score":0.90829998254776,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.7046964168548584},{"id":"https://openalex.org/keywords/parametric-statistics","display_name":"Parametric statistics","score":0.6525833010673523},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.6453644037246704},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.6298379898071289},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.6079675555229187},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.5291633605957031},{"id":"https://openalex.org/keywords/statistical-inference","display_name":"Statistical inference","score":0.5200504660606384},{"id":"https://openalex.org/keywords/parametric-model","display_name":"Parametric model","score":0.5011677742004395},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.46401891112327576},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.40536069869995117},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3701137900352478},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.35294175148010254},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3484874963760376},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.3359518349170685},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.10761985182762146}],"concepts":[{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.7046964168548584},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.6525833010673523},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.6453644037246704},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.6298379898071289},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.6079675555229187},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.5291633605957031},{"id":"https://openalex.org/C134261354","wikidata":"https://www.wikidata.org/wiki/Q938438","display_name":"Statistical inference","level":2,"score":0.5200504660606384},{"id":"https://openalex.org/C24574437","wikidata":"https://www.wikidata.org/wiki/Q7135228","display_name":"Parametric model","level":3,"score":0.5011677742004395},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.46401891112327576},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.40536069869995117},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3701137900352478},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.35294175148010254},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3484874963760376},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3359518349170685},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.10761985182762146},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1016/j.csda.2026.108445","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.csda.2026.108445","pdf_url":null,"source":{"id":"https://openalex.org/S132362803","display_name":"Computational Statistics & Data Analysis","issn_l":"0167-9473","issn":["0167-9473","1872-7352"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computational Statistics & Data Analysis","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:2408.14661","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2408.14661","pdf_url":"https://arxiv.org/pdf/2408.14661","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"pmh:oai:arXiv.org:2408.14661","is_oa":true,"landing_page_url":"https://arxiv.org/abs/2408.14661","pdf_url":"https://arxiv.org/pdf/2408.14661","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2408.14661","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2408.14661","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.1016/j.csda.2026.108445","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.csda.2026.108445","pdf_url":null,"source":{"id":"https://openalex.org/S132362803","display_name":"Computational Statistics & Data Analysis","issn_l":"0167-9473","issn":["0167-9473","1872-7352"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computational Statistics & Data Analysis","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320320290","display_name":"University of Oxford","ror":"https://ror.org/052gg0110"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W4225137382","https://openalex.org/W3112403041","https://openalex.org/W2763110165","https://openalex.org/W1974745849","https://openalex.org/W2043805242","https://openalex.org/W2049540116","https://openalex.org/W2530340704","https://openalex.org/W4399604658","https://openalex.org/W1999067727","https://openalex.org/W4211177544","https://openalex.org/W2018883342","https://openalex.org/W2016000144","https://openalex.org/W2893036933","https://openalex.org/W2064851486","https://openalex.org/W4254749579","https://openalex.org/W1999241502","https://openalex.org/W1734795241","https://openalex.org/W3000592230","https://openalex.org/W2071601825","https://openalex.org/W2080972498","https://openalex.org/W4410803023","https://openalex.org/W4411325050","https://openalex.org/W2087309226","https://openalex.org/W1988283120","https://openalex.org/W4205650551","https://openalex.org/W2036821522","https://openalex.org/W2116982066","https://openalex.org/W2165558339","https://openalex.org/W2968379181","https://openalex.org/W2033442452","https://openalex.org/W4206711483","https://openalex.org/W3005633550","https://openalex.org/W2203714058","https://openalex.org/W3177037454","https://openalex.org/W4255296637","https://openalex.org/W2165250182","https://openalex.org/W2115507118"],"related_works":["https://openalex.org/W2407375987","https://openalex.org/W3049691116","https://openalex.org/W2505726097","https://openalex.org/W2010643158","https://openalex.org/W2106867672","https://openalex.org/W3081214562","https://openalex.org/W4310268968","https://openalex.org/W2950975704","https://openalex.org/W4286981651","https://openalex.org/W4297815943"],"abstract_inverted_index":{"A":[0,58],"social":[1,10,126,174],"hierarchy":[2],"is":[3,149,170],"a":[4,15,18,24,50,142,163],"set":[5],"of":[6,47,71,105,109,119,124],"power":[7,33],"relations":[8,13],"between":[9],"actors.":[11],"These":[12,68],"form":[14],"partial":[16,51,59,76,87,135,146],"order,":[17,77],"transitively-closed":[19],"directed":[20,30],"acyclic":[21],"graph,":[22],"with":[23,78],"vertex":[25],"for":[26,134,176],"each":[27,96],"actor":[28],"and":[29,117,151,156,178],"edges":[31],"indicating":[32],"relations.":[34],"Partial":[35],"orders":[36,42],"are":[37,69,139,160],"more":[38],"general":[39],"than":[40],"total":[41,190],"(simple":[43],"rankings)":[44],"because":[45],"pairs":[46],"actors":[48,72,81,83,99,138],"in":[49,85,91,95,158],"order":[52,56,60,88,94,147],"may":[53],"have":[54],"no":[55],"relation.":[57],"can":[61],"be":[62],"estimated":[63,152],"using":[64,141,153,162],"sampled":[65],"linear":[66],"extensions.":[67],"lists":[70,118],"which":[73,188],"respect":[74],"the":[75,86,92,115,145],"higher":[79],"status":[80,112],"first:":[82],"ordered":[84],"must":[89],"appear":[90],"same":[93],"list;":[97],"unordered":[98],"swap":[100],"randomly":[101],"across":[102],"lists.":[103],"Examples":[104],"list-data":[106,159],"include":[107],"records":[108],"queues":[110],"(higher":[111],"individuals":[113],"at":[114],"front)":[116],"length":[120],"two":[121],"recording":[122],"outcomes":[123],"agonistic":[125],"interactions.":[127],"This":[128],"paper":[129],"develops":[130],"non-parametric":[131],"Bayesian":[132],"inference":[133],"orders.":[136,191],"Tied":[137],"clustered":[140],"Poisson-Dirichlet":[143],"process,":[144],"dimension":[148],"modeled":[150,161],"reversible-jump":[154],"MCMC,":[155],"errors":[157],"Mallow\u2019s":[164],"observation":[165],"model.":[166],"The":[167],"new":[168],"framework":[169],"used":[171],"to":[172],"estimate":[173],"hierarchies":[175],"humans":[177],"chimpanzees.":[179],"Statistical":[180],"tests":[181],"select":[182],"partial-order-based":[183],"models":[184],"over":[185],"well-known":[186],"alternatives":[187],"fit":[189]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-07-23T08:03:31.855105","created_date":"2024-09-21T00:00:00"}