{"id":"https://openalex.org/W7214946308","doi":"https://doi.org/10.48550/arxiv.2609.37996","title":"Stochastic Variational Inference for Vine Copula Distributional Regression","display_name":"Stochastic Variational Inference for Vine Copula Distributional Regression","publication_year":2026,"publication_date":"2026-09-29","ids":{"openalex":"https://openalex.org/W7214946308","doi":"https://doi.org/10.48550/arxiv.2609.37996"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2609.37996","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2609.37996","pdf_url":"https://arxiv.org/pdf/2609.37996","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/2609.37996","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5151639102","display_name":"Gianmarco Callegher","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I74656192","display_name":"University of G\u00f6ttingen","ror":"https://ror.org/01y9bpm73","country_code":"DE","type":"education","lineage":["https://openalex.org/I74656192"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Callegher, Gianmarco","raw_affiliation_strings":["University of G\u00f6ttingen"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of G\u00f6ttingen","institution_ids":["https://openalex.org/I74656192"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5009871877","display_name":"Thomas Kneib","orcid":"https://orcid.org/0000-0003-3390-0972","observed_orcids":["https://orcid.org/0000-0003-3390-0972"]},"institutions":[{"id":"https://openalex.org/I74656192","display_name":"University of G\u00f6ttingen","ror":"https://ror.org/01y9bpm73","country_code":"DE","type":"education","lineage":["https://openalex.org/I74656192"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Kneib, Thomas","raw_affiliation_strings":["University of G\u00f6ttingen"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of G\u00f6ttingen","institution_ids":["https://openalex.org/I74656192"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I74656192"],"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/T10282","display_name":"Financial Risk and Volatility Modeling","score":0.7846999764442444,"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"}},"topics":[{"id":"https://openalex.org/T10282","display_name":"Financial Risk and Volatility Modeling","score":0.7846999764442444,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.0625,"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/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.04619999974966049,"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/vine-copulas","display_name":"vine copulas","score":0.7990000247955322},{"id":"https://openalex.org/keywords/stochastic-variational-inference","display_name":"stochastic variational inference","score":0.8880000114440918},{"id":"https://openalex.org/keywords/effective-degrees-of-freedom","display_name":"effective degrees of freedom","score":0.7720000147819519},{"id":"https://openalex.org/keywords/tail-dependence","display_name":"tail dependence","score":0.6899999976158142},{"id":"https://openalex.org/keywords/asymmetric-dependence","display_name":"asymmetric dependence","score":0.7630000114440918},{"id":"https://openalex.org/keywords/meteorological-observations","display_name":"meteorological observations","score":0.546999990940094},{"id":"https://openalex.org/keywords/spatiotemporal-dependencies","display_name":"spatiotemporal dependencies","score":0.6159999966621399}],"concepts":[{"id":"https://openalex.org/C2779676228","wikidata":"https://www.wikidata.org/wiki/Q25303905","display_name":"Vine copula","level":3,"score":0.9175000190734863},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.697700023651123},{"id":"https://openalex.org/C17618745","wikidata":"https://www.wikidata.org/wiki/Q207509","display_name":"Copula (linguistics)","level":2,"score":0.6970999836921692},{"id":"https://openalex.org/C165216359","wikidata":"https://www.wikidata.org/wiki/Q670653","display_name":"Marginal distribution","level":3,"score":0.5350000262260437},{"id":"https://openalex.org/C162376815","wikidata":"https://www.wikidata.org/wiki/Q2158281","display_name":"Frequentist inference","level":4,"score":0.5242000222206116},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.5145999789237976},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.4957999885082245},{"id":"https://openalex.org/C43555835","wikidata":"https://www.wikidata.org/wiki/Q2300258","display_name":"Conditional probability distribution","level":2,"score":0.4717000126838684},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4611999988555908},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.44679999351501465},{"id":"https://openalex.org/C37616216","wikidata":"https://www.wikidata.org/wiki/Q3218363","display_name":"Lasso (programming language)","level":2,"score":0.4440999925136566},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.4189000129699707},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.4185999929904938},{"id":"https://openalex.org/C119043178","wikidata":"https://www.wikidata.org/wiki/Q320723","display_name":"Covariate","level":2,"score":0.3921000063419342},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.36070001125335693},{"id":"https://openalex.org/C177384507","wikidata":"https://www.wikidata.org/wiki/Q1149000","display_name":"Multivariate normal distribution","level":3,"score":0.334199994802475},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.3292999863624573},{"id":"https://openalex.org/C79772020","wikidata":"https://www.wikidata.org/wiki/Q5159264","display_name":"Conditional independence","level":2,"score":0.32010000944137573},{"id":"https://openalex.org/C18653775","wikidata":"https://www.wikidata.org/wiki/Q1333358","display_name":"Joint probability distribution","level":2,"score":0.31119999289512634},{"id":"https://openalex.org/C44882253","wikidata":"https://www.wikidata.org/wiki/Q3455882","display_name":"Multivariate adaptive regression splines","level":4,"score":0.30320000648498535},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.287200003862381},{"id":"https://openalex.org/C2777472644","wikidata":"https://www.wikidata.org/wiki/Q16968992","display_name":"Approximate inference","level":3,"score":0.28529998660087585},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.28349998593330383},{"id":"https://openalex.org/C74127309","wikidata":"https://www.wikidata.org/wiki/Q3455886","display_name":"Nonparametric regression","level":3,"score":0.2761000096797943},{"id":"https://openalex.org/C186215838","wikidata":"https://www.wikidata.org/wiki/Q772232","display_name":"Conditional expectation","level":2,"score":0.2759000062942505},{"id":"https://openalex.org/C55166926","wikidata":"https://www.wikidata.org/wiki/Q2892946","display_name":"Oracle","level":2,"score":0.27399998903274536},{"id":"https://openalex.org/C129844170","wikidata":"https://www.wikidata.org/wiki/Q41299","display_name":"Quadratic equation","level":2,"score":0.2721000015735626},{"id":"https://openalex.org/C65778772","wikidata":"https://www.wikidata.org/wiki/Q12345341","display_name":"Asymptotic distribution","level":3,"score":0.267300009727478},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.2581000030040741},{"id":"https://openalex.org/C203223496","wikidata":"https://www.wikidata.org/wiki/Q4681344","display_name":"Additive model","level":2,"score":0.25589999556541443}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2609.37996","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2609.37996","pdf_url":"https://arxiv.org/pdf/2609.37996","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.2609.37996","doi":"https://doi.org/10.48550/arxiv.2609.37996","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2609.37996","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:2609.37996","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2609.37996","pdf_url":"https://arxiv.org/pdf/2609.37996","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":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7214946308.pdf","grobid_xml":"https://content.openalex.org/works/W7214946308.grobid-xml"},"referenced_works_count":3,"referenced_works":["https://openalex.org/W4296967842","https://openalex.org/W4405433446","https://openalex.org/W7140286115"],"related_works":["https://openalex.org/W627210737","https://openalex.org/W2952553869","https://openalex.org/W2140660104","https://openalex.org/W7140286115","https://openalex.org/W3200167914","https://openalex.org/W7148279681","https://openalex.org/W4405433446","https://openalex.org/W4391158651","https://openalex.org/W4298077312","https://openalex.org/W2949023603"],"abstract_inverted_index":{"Structured":[0],"additive":[1,29,57],"distributional":[2,30],"regression":[3,31],"flexibly":[4],"relates":[5],"all":[6],"parameters":[7],"of":[8,126],"a":[9,26,55,80,174],"conditional":[10,148],"response":[11],"distribution":[12],"to":[13,141,166],"covariates,":[14],"but":[15],"multivariate":[16,27],"extensions":[17],"remain":[18],"challenging":[19],"when":[20],"dependence":[21,73],"is":[22],"complex.":[23],"We":[24,106],"propose":[25],"structured":[28,56],"model":[32,60],"based":[33,86,122],"on":[34,87,123],"regular":[35],"vine":[36,39,104,110,175],"copulas.":[37],"Different":[38],"edges":[40],"may":[41,50],"use":[42],"different":[43],"pair-copula":[44,48,99,116],"families,":[45],"while":[46],"every":[47],"parameter":[49],"vary":[51],"with":[52,67,182],"covariates":[53],"through":[54],"predictor.":[58],"The":[59],"therefore":[61],"accommodates":[62],"heterogeneous":[63],"marginal":[64],"distributions":[65],"together":[66],"pair-specific":[68],"asymmetric,":[69],"tail-dependent,":[70],"and":[71,118,131,157,178,186],"covariate-dependent":[72,115],"structures.":[74],"For":[75],"scalable":[76],"inference,":[77],"we":[78],"develop":[79],"tree-wise":[81,137],"stochastic":[82],"variational":[83,90],"inference":[84],"procedure":[85],"component-specific":[88],"Gaussian":[89,177],"approximations.":[91],"Marginal":[92],"models":[93],"are":[94],"estimated":[95],"first,":[96],"followed":[97],"by":[98,112],"regressions":[100,117],"sequentially":[101],"along":[102],"the":[103,136,145,171],"trees.":[105],"also":[107],"adapt":[108],"sequential":[109],"selection":[111],"fitting":[113],"candidate":[114],"using":[119,144],"information":[120],"criteria":[121],"effective":[124],"degrees":[125],"freedom":[127],"for":[128,161],"both":[129],"family":[130],"tree":[132],"selection.":[133],"In":[134],"simulations,":[135],"estimator":[138],"remains":[139],"close":[140],"an":[142],"oracle":[143],"true":[146],"recursive":[147],"inputs,":[149],"whereas":[150],"global":[151],"refinement":[152],"yields":[153,173],"more":[154],"concentrated":[155],"approximations":[156],"lower":[158],"frequentist":[159],"coverage":[160],"upstream":[162],"components.":[163],"An":[164],"application":[165],"six-dimensional":[167],"meteorological":[168],"data":[169],"from":[170],"Netherlands":[172],"combining":[176],"non-Gaussian":[179],"pair":[180],"copulas,":[181],"pronounced":[183],"nonlinear":[184],"spatial":[185],"temporal":[187],"variation":[188],"in":[189],"dependence.":[190]},"counts_by_year":[],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2026-10-01T00:00:00"}
