{"id":"https://openalex.org/W3093491587","doi":"https://doi.org/10.1145/3340531.3412129","title":"A Framework for Analyzing the Impact of Missing Data in Predictive Models","display_name":"A Framework for Analyzing the Impact of Missing Data in Predictive Models","publication_year":2020,"publication_date":"2020-10-19","ids":{"openalex":"https://openalex.org/W3093491587","doi":"https://doi.org/10.1145/3340531.3412129","mag":"3093491587"},"language":"en","primary_location":{"id":"doi:10.1145/3340531.3412129","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3340531.3412129","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th ACM International Conference on Information & Knowledge Management","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5055229914","display_name":"Fabiola Santore","orcid":null},"institutions":[{"id":"https://openalex.org/I52418104","display_name":"Universidade Federal do Paran\u00e1","ror":"https://ror.org/05syd6y78","country_code":"BR","type":"education","lineage":["https://openalex.org/I52418104"]}],"countries":["BR"],"is_corresponding":true,"raw_author_name":"Fabiola Santore","raw_affiliation_strings":["Federal University of Paran\u00e1, Curitiba, Brazil"],"affiliations":[{"raw_affiliation_string":"Federal University of Paran\u00e1, Curitiba, Brazil","institution_ids":["https://openalex.org/I52418104"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069121883","display_name":"Eduardo Cunha de Almeida","orcid":"https://orcid.org/0000-0002-6644-956X"},"institutions":[{"id":"https://openalex.org/I52418104","display_name":"Universidade Federal do Paran\u00e1","ror":"https://ror.org/05syd6y78","country_code":"BR","type":"education","lineage":["https://openalex.org/I52418104"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Eduardo C. de Almeida","raw_affiliation_strings":["Federal University of Paran\u00e1, Curitiba, Brazil"],"affiliations":[{"raw_affiliation_string":"Federal University of Paran\u00e1, Curitiba, Brazil","institution_ids":["https://openalex.org/I52418104"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082508212","display_name":"Wagner Hugo Bonat","orcid":"https://orcid.org/0000-0002-0349-7054"},"institutions":[{"id":"https://openalex.org/I52418104","display_name":"Universidade Federal do Paran\u00e1","ror":"https://ror.org/05syd6y78","country_code":"BR","type":"education","lineage":["https://openalex.org/I52418104"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Wagner H. Bonat","raw_affiliation_strings":["Federal University of Paran\u00e1, Curitiba, Brazil"],"affiliations":[{"raw_affiliation_string":"Federal University of Paran\u00e1, Curitiba, Brazil","institution_ids":["https://openalex.org/I52418104"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065023756","display_name":"Eduardo H. M. Pena","orcid":"https://orcid.org/0000-0002-4852-3113"},"institutions":[{"id":"https://openalex.org/I1283613182","display_name":"Universidade Tecnol\u00f3gica Federal do Paran\u00e1","ror":"https://ror.org/002v2kq79","country_code":"BR","type":"education","lineage":["https://openalex.org/I1283613182"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Eduardo H. M. Pena","raw_affiliation_strings":["Federal University of Technology - Paran\u00e1, Toledo, Brazil"],"affiliations":[{"raw_affiliation_string":"Federal University of Technology - Paran\u00e1, Toledo, Brazil","institution_ids":["https://openalex.org/I1283613182"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5038884704","display_name":"Luiz S. Oliveira","orcid":"https://orcid.org/0000-0002-0595-5370"},"institutions":[{"id":"https://openalex.org/I52418104","display_name":"Universidade Federal do Paran\u00e1","ror":"https://ror.org/05syd6y78","country_code":"BR","type":"education","lineage":["https://openalex.org/I52418104"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Luiz Eduardo S. de Oliveira","raw_affiliation_strings":["Federal University of Paran\u00e1, Curitiba, Brazil"],"affiliations":[{"raw_affiliation_string":"Federal University of Paran\u00e1, Curitiba, Brazil","institution_ids":["https://openalex.org/I52418104"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5055229914"],"corresponding_institution_ids":["https://openalex.org/I52418104"],"apc_list":null,"apc_paid":null,"fwci":0.8639,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.76268649,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"2209","last_page":"2212"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10243","display_name":"Statistical Methods and Bayesian Inference","score":0.9983000159263611,"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/T10243","display_name":"Statistical Methods and Bayesian Inference","score":0.9983000159263611,"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/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9973999857902527,"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/T10136","display_name":"Statistical Methods and Inference","score":0.9909999966621399,"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/missing-data","display_name":"Missing data","score":0.865935206413269},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.6347984671592712},{"id":"https://openalex.org/keywords/predictive-power","display_name":"Predictive power","score":0.6210943460464478},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6185379028320312},{"id":"https://openalex.org/keywords/logistic-regression","display_name":"Logistic regression","score":0.5419891476631165},{"id":"https://openalex.org/keywords/bernoullis-principle","display_name":"Bernoulli's principle","score":0.5130881071090698},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4304157495498657},{"id":"https://openalex.org/keywords/binary-data","display_name":"Binary data","score":0.42828407883644104},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.4236256778240204},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.42013275623321533},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.40509843826293945},{"id":"https://openalex.org/keywords/binary-number","display_name":"Binary number","score":0.39378178119659424},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.29798638820648193},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.26998090744018555},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.25336676836013794},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.0830400288105011}],"concepts":[{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.865935206413269},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.6347984671592712},{"id":"https://openalex.org/C2778136018","wikidata":"https://www.wikidata.org/wiki/Q10350689","display_name":"Predictive power","level":2,"score":0.6210943460464478},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6185379028320312},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.5419891476631165},{"id":"https://openalex.org/C152361515","wikidata":"https://www.wikidata.org/wiki/Q181328","display_name":"Bernoulli's principle","level":2,"score":0.5130881071090698},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4304157495498657},{"id":"https://openalex.org/C2779190172","wikidata":"https://www.wikidata.org/wiki/Q4913888","display_name":"Binary data","level":3,"score":0.42828407883644104},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.4236256778240204},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.42013275623321533},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.40509843826293945},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.39378178119659424},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.29798638820648193},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.26998090744018555},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.25336676836013794},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0830400288105011},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0},{"id":"https://openalex.org/C94375191","wikidata":"https://www.wikidata.org/wiki/Q11205","display_name":"Arithmetic","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3340531.3412129","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3340531.3412129","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th ACM International Conference on Information & Knowledge Management","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W1977185509","https://openalex.org/W1983479840","https://openalex.org/W2044758663","https://openalex.org/W2162210260","https://openalex.org/W2171710987","https://openalex.org/W2486604124","https://openalex.org/W2542806636","https://openalex.org/W2624822310","https://openalex.org/W2783365435","https://openalex.org/W2932881901","https://openalex.org/W3006153429","https://openalex.org/W3197494818"],"related_works":["https://openalex.org/W4380150146","https://openalex.org/W3024870410","https://openalex.org/W2410652950","https://openalex.org/W4283773154","https://openalex.org/W3139174110","https://openalex.org/W4289597203","https://openalex.org/W2085630472","https://openalex.org/W1977098485","https://openalex.org/W3216372614","https://openalex.org/W2187819724"],"abstract_inverted_index":{"We":[0],"propose":[1],"a":[2,61],"stochastic":[3],"framework":[4,19,82],"to":[5,68,73,86],"evaluate":[6],"the":[7,13,27,33,38,41,44,74,81,99,105,108,116,129,133,139],"impact":[8],"of":[9,15,23,26,37,90,118,135],"missing":[10,56,70,119],"data":[11,28,120],"on":[12,98],"performance":[14],"predictive":[16,50,91,109],"models.":[17],"The":[18,55,111],"allows":[20,66],"full":[21],"control":[22],"important":[24],"aspects":[25],"set":[29],"structure.":[30],"These":[31],"include":[32],"number":[34,134],"and":[35,47,52,77,103],"type":[36],"input":[39,45,136],"variables,":[40],"correlation":[42],"between":[43],"variables":[46,137],"their":[48],"general":[49],"power,":[51],"sample":[53,124],"size.":[54],"process":[57],"is":[58],"generated":[59],"from":[60],"multivariate":[62],"Bernoulli":[63],"distribution,":[64],"which":[65],"us":[67],"simulate":[69],"patterns":[71],"corresponding":[72],"MCAR,":[75],"MAR":[76],"MNAR":[78],"mechanisms.":[79],"Although":[80],"may":[83],"be":[84],"applied":[85],"virtually":[87],"all":[88],"types":[89],"models,":[92],"in":[93],"this":[94],"article,":[95],"we":[96],"focus":[97],"logistic":[100],"regression":[101],"model":[102],"choose":[104],"accuracy":[106,140],"as":[107,126,132],"measure.":[110],"simulation":[112],"results":[113],"show":[114],"that":[115],"effects":[117],"disappear":[121],"for":[122,143],"large":[123],"sizes,":[125],"expected.":[127],"On":[128],"other":[130],"hand,":[131],"increases,":[138],"decreases":[141],"mainly":[142],"binary":[144],"inputs.":[145]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}