{"id":"https://openalex.org/W3080534420","doi":"https://doi.org/10.1145/3394486.3403106","title":"Missing Value Imputation for Mixed Data via Gaussian Copula","display_name":"Missing Value Imputation for Mixed Data via Gaussian Copula","publication_year":2020,"publication_date":"2020-08-20","ids":{"openalex":"https://openalex.org/W3080534420","doi":"https://doi.org/10.1145/3394486.3403106","mag":"3080534420"},"language":"en","primary_location":{"id":"doi:10.1145/3394486.3403106","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3394486.3403106","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3394486.3403106","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery &amp; Data Mining","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3394486.3403106","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5030009166","display_name":"Yuxuan Zhao","orcid":"https://orcid.org/0000-0001-8410-8534"},"institutions":[{"id":"https://openalex.org/I205783295","display_name":"Cornell University","ror":"https://ror.org/05bnh6r87","country_code":"US","type":"education","lineage":["https://openalex.org/I205783295"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yuxuan Zhao","raw_affiliation_strings":["Cornell University, Ithaca, NY, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Cornell University, Ithaca, NY, USA","institution_ids":["https://openalex.org/I205783295"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5084564811","display_name":"Madeleine Udell","orcid":"https://orcid.org/0000-0002-3985-915X"},"institutions":[{"id":"https://openalex.org/I205783295","display_name":"Cornell University","ror":"https://ror.org/05bnh6r87","country_code":"US","type":"education","lineage":["https://openalex.org/I205783295"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Madeleine Udell","raw_affiliation_strings":["Cornell University, Ithaca, NY, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Cornell University, Ithaca, NY, USA","institution_ids":["https://openalex.org/I205783295"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I205783295"],"apc_list":null,"apc_paid":null,"fwci":18.2989,"has_fulltext":true,"cited_by_count":56,"citation_normalized_percentile":{"value":0.9974271,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"636","last_page":"646"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":0.9916999936103821,"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/T10136","display_name":"Statistical Methods and Inference","score":0.9916999936103821,"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/T10243","display_name":"Statistical Methods and Bayesian Inference","score":0.9904000163078308,"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/T12303","display_name":"Tensor decomposition and applications","score":0.9668999910354614,"subfield":{"id":"https://openalex.org/subfields/2605","display_name":"Computational Mathematics"},"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/imputation","display_name":"Imputation (statistics)","score":0.7937650680541992},{"id":"https://openalex.org/keywords/missing-data","display_name":"Missing data","score":0.742776095867157},{"id":"https://openalex.org/keywords/copula","display_name":"Copula (linguistics)","score":0.6425658464431763},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5803847908973694},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.534525454044342},{"id":"https://openalex.org/keywords/ordinal-data","display_name":"Ordinal data","score":0.5201878547668457},{"id":"https://openalex.org/keywords/expectation\u2013maximization-algorithm","display_name":"Expectation\u2013maximization algorithm","score":0.46829739212989807},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.4616263210773468},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4090896248817444},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.35970696806907654},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3400818705558777},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.24518808722496033},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.18704408407211304},{"id":"https://openalex.org/keywords/maximum-likelihood","display_name":"Maximum likelihood","score":0.12799665331840515}],"concepts":[{"id":"https://openalex.org/C58041806","wikidata":"https://www.wikidata.org/wiki/Q1660484","display_name":"Imputation (statistics)","level":3,"score":0.7937650680541992},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.742776095867157},{"id":"https://openalex.org/C17618745","wikidata":"https://www.wikidata.org/wiki/Q207509","display_name":"Copula (linguistics)","level":2,"score":0.6425658464431763},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5803847908973694},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.534525454044342},{"id":"https://openalex.org/C85461838","wikidata":"https://www.wikidata.org/wiki/Q7100785","display_name":"Ordinal data","level":2,"score":0.5201878547668457},{"id":"https://openalex.org/C182081679","wikidata":"https://www.wikidata.org/wiki/Q1275153","display_name":"Expectation\u2013maximization algorithm","level":3,"score":0.46829739212989807},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.4616263210773468},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4090896248817444},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.35970696806907654},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3400818705558777},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.24518808722496033},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.18704408407211304},{"id":"https://openalex.org/C49781872","wikidata":"https://www.wikidata.org/wiki/Q1045555","display_name":"Maximum likelihood","level":2,"score":0.12799665331840515},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3394486.3403106","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3394486.3403106","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3394486.3403106","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery &amp; Data Mining","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3394486.3403106","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3394486.3403106","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3394486.3403106","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery &amp; Data Mining","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3083072850","display_name":null,"funder_award_id":"CCF-1740822","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G385906281","display_name":null,"funder_award_id":"IIS-1943131 and CCF-1740822","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G4296480827","display_name":null,"funder_award_id":"FA8750-17-2-0101","funder_id":"https://openalex.org/F4320332180","funder_display_name":"Defense Advanced Research Projects Agency"},{"id":"https://openalex.org/G4317805186","display_name":"CAREER: accelerating machine learning with low dimensional structure","funder_award_id":"1943131","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G4713059963","display_name":null,"funder_award_id":"FA8750","funder_id":"https://openalex.org/F4320332180","funder_display_name":"Defense Advanced Research Projects Agency"},{"id":"https://openalex.org/G836416240","display_name":null,"funder_award_id":"IIS-1943131","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320332180","display_name":"Defense Advanced Research Projects Agency","ror":"https://ror.org/02caytj08"},{"id":"https://openalex.org/F4320334164","display_name":"Simons Institute for the Theory of Computing, University of California Berkeley","ror":null},{"id":"https://openalex.org/F4320334506","display_name":"Canadian Institutes of Health Research","ror":"https://ror.org/01gavpb45"},{"id":"https://openalex.org/F4320337345","display_name":"Office of Naval Research","ror":"https://ror.org/00rk2pe57"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3080534420.pdf","grobid_xml":"https://content.openalex.org/works/W3080534420.grobid-xml"},"referenced_works_count":33,"referenced_works":["https://openalex.org/W1480812382","https://openalex.org/W1495663238","https://openalex.org/W1509689762","https://openalex.org/W1960964799","https://openalex.org/W1974788265","https://openalex.org/W1976618413","https://openalex.org/W1992535794","https://openalex.org/W2019935777","https://openalex.org/W2025533349","https://openalex.org/W2030075579","https://openalex.org/W2044758663","https://openalex.org/W2064186732","https://openalex.org/W2070609300","https://openalex.org/W2085893390","https://openalex.org/W2087160118","https://openalex.org/W2115098571","https://openalex.org/W2133999183","https://openalex.org/W2137892504","https://openalex.org/W2146130798","https://openalex.org/W2164616133","https://openalex.org/W2185212216","https://openalex.org/W2611328865","https://openalex.org/W2616032753","https://openalex.org/W2805127664","https://openalex.org/W2888052088","https://openalex.org/W2921545086","https://openalex.org/W2962806864","https://openalex.org/W2999625102","https://openalex.org/W3003365835","https://openalex.org/W3100838142","https://openalex.org/W3197494818","https://openalex.org/W4230254221","https://openalex.org/W4232023503"],"related_works":["https://openalex.org/W2181530120","https://openalex.org/W4211215373","https://openalex.org/W2024529227","https://openalex.org/W2055961818","https://openalex.org/W1574575415","https://openalex.org/W3144172081","https://openalex.org/W3179858851","https://openalex.org/W3028371478","https://openalex.org/W2081476516","https://openalex.org/W2581984549"],"abstract_inverted_index":{"Missing":[0],"data":[1,10,19,69],"imputation":[2,31,139],"forms":[3],"the":[4,38,44,48,117,131],"first":[5],"critical":[6],"step":[7],"of":[8,133],"many":[9,89],"analysis":[11],"pipelines.":[12],"The":[13,65,113],"challenge":[14],"is":[15],"greatest":[16],"for":[17,30,36,80,141],"mixed":[18,68,111,142],"sets,":[20],"including":[21,91],"real,":[22],"Boolean,":[23],"and":[24,83,127],"ordinal":[25,86],"data,":[26],"where":[27],"standard":[28],"techniques":[29],"fail":[32],"basic":[33],"sanity":[34],"checks:":[35],"example,":[37],"imputed":[39],"values":[40],"may":[41],"not":[42],"follow":[43],"same":[45],"distributions":[46],"as":[47,70,94],"data.":[49,112,143],"This":[50,74],"paper":[51],"proposes":[52],"a":[53,71,95],"new":[54],"semiparametric":[55],"algorithm":[56,66,104,136],"to":[57,105,137],"impute":[58],"missing":[59],"values,":[60],"with":[61,88],"no":[62],"tuning":[63],"parameters.":[64],"models":[67],"Gaussian":[72],"copula.":[73],"model":[75,115],"can":[76,84],"fit":[77],"arbitrary":[78],"marginals":[79],"continuous":[81],"variables":[82,87,93],"handle":[85],"levels,":[90],"Boolean":[92],"special":[96],"case.":[97],"We":[98],"develop":[99],"an":[100],"efficient":[101],"approximate":[102],"EM":[103],"estimate":[106],"copula":[107],"parameters":[108],"from":[109],"incomplete":[110],"resulting":[114],"reveals":[116],"statistical":[118],"associations":[119],"among":[120],"variables.":[121],"Experimental":[122],"results":[123],"on":[124],"several":[125],"synthetic":[126],"real":[128],"datasets":[129],"show":[130],"superiority":[132],"our":[134],"proposed":[135],"state-of-the-art":[138],"algorithms":[140]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":11},{"year":2024,"cited_by_count":9},{"year":2023,"cited_by_count":14},{"year":2022,"cited_by_count":8},{"year":2021,"cited_by_count":7},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
