{"id":"https://openalex.org/W7223129293","doi":"https://doi.org/10.48550/arxiv.2610.28260","title":"Bayesian nonparametric mixtures of asymmetric Archimedean copulas","display_name":"Bayesian nonparametric mixtures of asymmetric Archimedean copulas","publication_year":2026,"publication_date":"2026-10-20","ids":{"openalex":"https://openalex.org/W7223129293","doi":"https://doi.org/10.48550/arxiv.2610.28260"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2610.28260","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2610.28260","pdf_url":"https://arxiv.org/pdf/2610.28260","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2610.28260","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5012428925","display_name":"Radu V. Craiu","orcid":"https://orcid.org/0000-0002-1348-8063","observed_orcids":["https://orcid.org/0000-0002-1348-8063"]},"institutions":[{"id":"https://openalex.org/I185261750","display_name":"University of Toronto","ror":"https://ror.org/03dbr7087","country_code":"CA","type":"education","lineage":["https://openalex.org/I185261750"]},{"id":"https://openalex.org/I187481652","display_name":"Instituto Tecnol\u00f3gico Aut\u00f3nomo de M\u00e9xico","ror":"https://ror.org/029md1766","country_code":"MX","type":"education","lineage":["https://openalex.org/I187481652"]}],"countries":["CA","MX"],"is_corresponding":false,"raw_author_name":"Craiu, Radu V.","raw_affiliation_strings":["University of Toronto & ITAM"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Toronto & ITAM","institution_ids":["https://openalex.org/I185261750","https://openalex.org/I187481652"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5039175972","display_name":"Luis Enrique Nieto-Barajas","orcid":"https://orcid.org/0000-0002-0859-7679","observed_orcids":["https://orcid.org/0000-0002-0859-7679"]},"institutions":[{"id":"https://openalex.org/I185261750","display_name":"University of Toronto","ror":"https://ror.org/03dbr7087","country_code":"CA","type":"education","lineage":["https://openalex.org/I185261750"]},{"id":"https://openalex.org/I187481652","display_name":"Instituto Tecnol\u00f3gico Aut\u00f3nomo de M\u00e9xico","ror":"https://ror.org/029md1766","country_code":"MX","type":"education","lineage":["https://openalex.org/I187481652"]}],"countries":["CA","MX"],"is_corresponding":false,"raw_author_name":"Nieto-Barajas, Luis E.","raw_affiliation_strings":["University of Toronto & ITAM"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Toronto & ITAM","institution_ids":["https://openalex.org/I185261750","https://openalex.org/I187481652"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.7954000234603882,"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"}},"topics":[{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.7954000234603882,"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"}},{"id":"https://openalex.org/T10282","display_name":"Financial Risk and Volatility Modeling","score":0.18479999899864197,"subfield":{"id":"https://openalex.org/subfields/2003","display_name":"Finance"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10968","display_name":"Statistical Distribution Estimation and Applications","score":0.004600000102072954,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/archimedean-copulas","display_name":"Archimedean copulas","score":0.9419999718666077},{"id":"https://openalex.org/keywords/asymmetric-dependence","display_name":"asymmetric dependence","score":0.6269999742507935},{"id":"https://openalex.org/keywords/kendall-s-tau","display_name":"Kendall's tau","score":0.7149999737739563},{"id":"https://openalex.org/keywords/tail-dependence-coefficient","display_name":"tail dependence coefficient","score":0.7680000066757202},{"id":"https://openalex.org/keywords/poisson-dirichlet-process","display_name":"Poisson-Dirichlet process","score":0.9810000061988831},{"id":"https://openalex.org/keywords/bayesian-nonparametric-models","display_name":"Bayesian nonparametric models","score":0.734000027179718}],"concepts":[{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.7968999743461609},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.7095999717712402},{"id":"https://openalex.org/C17618745","wikidata":"https://www.wikidata.org/wiki/Q207509","display_name":"Copula (linguistics)","level":2,"score":0.7087000012397766},{"id":"https://openalex.org/C102366305","wikidata":"https://www.wikidata.org/wiki/Q1097688","display_name":"Nonparametric statistics","level":2,"score":0.6072999835014343},{"id":"https://openalex.org/C79772020","wikidata":"https://www.wikidata.org/wiki/Q5159264","display_name":"Conditional independence","level":2,"score":0.5450999736785889},{"id":"https://openalex.org/C2777606061","wikidata":"https://www.wikidata.org/wiki/Q16991115","display_name":"Tail dependence","level":3,"score":0.46480000019073486},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.4641000032424927},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4537999927997589},{"id":"https://openalex.org/C190470478","wikidata":"https://www.wikidata.org/wiki/Q2370229","display_name":"Invariant (physics)","level":2,"score":0.4041000008583069},{"id":"https://openalex.org/C138777275","wikidata":"https://www.wikidata.org/wiki/Q6884054","display_name":"Mixing (physics)","level":2,"score":0.36820000410079956},{"id":"https://openalex.org/C18653775","wikidata":"https://www.wikidata.org/wiki/Q1333358","display_name":"Joint probability distribution","level":2,"score":0.3668000102043152},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.364300012588501},{"id":"https://openalex.org/C44492722","wikidata":"https://www.wikidata.org/wiki/Q327069","display_name":"Conditional probability","level":2,"score":0.3409999907016754},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.31679999828338623},{"id":"https://openalex.org/C43555835","wikidata":"https://www.wikidata.org/wiki/Q2300258","display_name":"Conditional probability distribution","level":2,"score":0.31040000915527344},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3066999912261963},{"id":"https://openalex.org/C35651441","wikidata":"https://www.wikidata.org/wiki/Q625303","display_name":"Independence (probability theory)","level":2,"score":0.3005000054836273},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.28839999437332153},{"id":"https://openalex.org/C186215838","wikidata":"https://www.wikidata.org/wiki/Q772232","display_name":"Conditional expectation","level":2,"score":0.2825999855995178},{"id":"https://openalex.org/C165216359","wikidata":"https://www.wikidata.org/wiki/Q670653","display_name":"Marginal distribution","level":3,"score":0.27149999141693115},{"id":"https://openalex.org/C57830394","wikidata":"https://www.wikidata.org/wiki/Q278079","display_name":"Posterior probability","level":3,"score":0.2694000005722046},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.26570001244544983}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2610.28260","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2610.28260","pdf_url":"https://arxiv.org/pdf/2610.28260","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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.2610.28260","doi":"https://doi.org/10.48550/arxiv.2610.28260","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2610.28260","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2610.28260","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2610.28260","pdf_url":"https://arxiv.org/pdf/2610.28260","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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"},"sustainable_development_goals":[],"study_designs":[],"awards":[{"id":"https://openalex.org/G3757647470","display_name":null,"funder_award_id":"RGPIN-2024-04506","funder_id":"https://openalex.org/F4320334593","funder_display_name":"Natural Sciences and Engineering Research Council of Canada"}],"funders":[{"id":"https://openalex.org/F4320334593","display_name":"Natural Sciences and Engineering Research Council of Canada","ror":"https://ror.org/01h531d29"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7223129293.pdf","grobid_xml":"https://content.openalex.org/works/W7223129293.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4411639396","https://openalex.org/W2129674616","https://openalex.org/W2078484714","https://openalex.org/W7220961230","https://openalex.org/W2980764321","https://openalex.org/W2044146548","https://openalex.org/W2389716411","https://openalex.org/W2275369362","https://openalex.org/W2076562295","https://openalex.org/W6887869723"],"abstract_inverted_index":{"Archimedean":[0,26,47,85],"copulas":[1,27,37],"are":[2,28],"exchangeable:":[3],"a":[4,56,61,88,93,118,151],"single":[5],"generator":[6,103],"parameter":[7,104],"controls":[8],"every":[9],"pairwise":[10,71,137],"association,":[11],"and":[12,59,81,105,127,143,159],"the":[13,30,42,53,65,78,83,101,106,110,114,133,136,140,144,167,176],"copula":[14,86,102,134],"is":[15,33],"invariant":[16],"under":[17],"permutations":[18],"of":[19,25,45,129],"its":[20],"arguments.":[21],"Even":[22],"when":[23],"mixtures":[24],"formulated,":[29],"exchangeability":[31],"feature":[32],"inherited.":[34],"Non":[35],"exchangeable":[36,177],"can":[38],"be":[39],"defined":[40],"through":[41],"Khoudraji":[43,168],"transformation":[44,58],"an":[46],"copula.":[48,67],"This":[49],"relies":[50],"in":[51],"replacing":[52],"arguments":[54],"by":[55,98,124],"power":[57,79],"adding":[60],"factor":[62],"based":[63],"on":[64,77],"independence":[66],"The":[68,163],"construction":[69],"produces":[70],"Kendall":[72],"tau":[73],"coefficients":[74],"that":[75,108,166],"depend":[76],"parameters":[80,107],"recovers":[82],"original":[84],"as":[87,117],"particular":[89],"case.":[90],"We":[91,112,131,154],"propose":[92],"Bayesian":[94,152],"nonparametric":[95],"mixture":[96,169],"model":[97],"mixing":[99,119],"over":[100],"characterize":[109],"asymmetry.":[111],"use":[113],"Poisson--Dirichlet":[115],"process":[116],"distribution,":[120],"which":[121],"groups":[122],"observations":[123],"both":[125],"strength":[126],"direction":[128],"dependence.":[130],"derive":[132],"density,":[135],"association":[138],"measures,":[139],"tail":[141],"coefficients,":[142],"full":[145],"posterior":[146],"conditional":[147],"distributions":[148],"to":[149],"perform":[150],"analysis.":[153],"carry":[155],"out":[156],"simulation":[157],"studies":[158],"real":[160],"data":[161],"analyses.":[162],"results":[164],"show":[165],"improves":[170],"greatly":[171],"on,":[172],"or":[173],"agrees":[174],"with,":[175],"fit.":[178]},"counts_by_year":[],"updated_date":"2026-10-28T10:33:24.094632","created_date":"2026-10-22T00:00:00"}