{"id":"https://openalex.org/W3046138841","doi":"https://doi.org/10.1080/24725854.2020.1802792","title":"Copula-based multi-event modeling and prediction using fleet service records","display_name":"Copula-based multi-event modeling and prediction using fleet service records","publication_year":2020,"publication_date":"2020-07-28","ids":{"openalex":"https://openalex.org/W3046138841","doi":"https://doi.org/10.1080/24725854.2020.1802792","mag":"3046138841"},"language":"en","primary_location":{"id":"doi:10.1080/24725854.2020.1802792","is_oa":false,"landing_page_url":"https://doi.org/10.1080/24725854.2020.1802792","pdf_url":null,"source":{"id":"https://openalex.org/S4210225672","display_name":"IISE Transactions","issn_l":"2472-5854","issn":["2472-5854","2472-5862"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IISE Transactions","raw_type":"journal-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/A5064272669","display_name":"Akash Deep","orcid":"https://orcid.org/0000-0001-8678-2303"},"institutions":[{"id":"https://openalex.org/I135310074","display_name":"University of Wisconsin\u2013Madison","ror":"https://ror.org/01y2jtd41","country_code":"US","type":"education","lineage":["https://openalex.org/I135310074"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Akash Deep","raw_affiliation_strings":["Department of Industrial and Systems Engineering, University of Wisconsin Madison, Madison, WI, USA"],"raw_orcid":"https://orcid.org/0000-0001-8678-2303","affiliations":[{"raw_affiliation_string":"Department of Industrial and Systems Engineering, University of Wisconsin Madison, Madison, WI, USA","institution_ids":["https://openalex.org/I135310074"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000499123","display_name":"Shiyu Zhou","orcid":"https://orcid.org/0000-0002-5902-8812"},"institutions":[{"id":"https://openalex.org/I135310074","display_name":"University of Wisconsin\u2013Madison","ror":"https://ror.org/01y2jtd41","country_code":"US","type":"education","lineage":["https://openalex.org/I135310074"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Shiyu Zhou","raw_affiliation_strings":["Department of Industrial and Systems Engineering, University of Wisconsin Madison, Madison, WI, USA"],"raw_orcid":"https://orcid.org/0000-0002-5902-8812","affiliations":[{"raw_affiliation_string":"Department of Industrial and Systems Engineering, University of Wisconsin Madison, Madison, WI, USA","institution_ids":["https://openalex.org/I135310074"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5069175463","display_name":"Dharmaraj Veeramani","orcid":"https://orcid.org/0000-0003-3796-288X"},"institutions":[{"id":"https://openalex.org/I135310074","display_name":"University of Wisconsin\u2013Madison","ror":"https://ror.org/01y2jtd41","country_code":"US","type":"education","lineage":["https://openalex.org/I135310074"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dharmaraj Veeramani","raw_affiliation_strings":["Department of Industrial and Systems Engineering, University of Wisconsin Madison, Madison, WI, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Industrial and Systems Engineering, University of Wisconsin Madison, Madison, WI, USA","institution_ids":["https://openalex.org/I135310074"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5000499123"],"corresponding_institution_ids":["https://openalex.org/I135310074"],"apc_list":null,"apc_paid":null,"fwci":0.3814,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.60791237,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":98},"biblio":{"volume":"53","issue":"9","first_page":"1023","last_page":"1036"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10780","display_name":"Reliability and Maintenance Optimization","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/2213","display_name":"Safety, Risk, Reliability and Quality"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10780","display_name":"Reliability and Maintenance Optimization","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/2213","display_name":"Safety, Risk, Reliability and Quality"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12423","display_name":"Software Reliability and Analysis Research","score":0.9941999912261963,"subfield":{"id":"https://openalex.org/subfields/1712","display_name":"Software"},"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/T11357","display_name":"Risk and Safety Analysis","score":0.9854000210762024,"subfield":{"id":"https://openalex.org/subfields/1804","display_name":"Statistics, Probability and Uncertainty"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/copula","display_name":"Copula (linguistics)","score":0.8329477310180664},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6211632490158081},{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.5559066534042358},{"id":"https://openalex.org/keywords/proportional-hazards-model","display_name":"Proportional hazards model","score":0.5368552207946777},{"id":"https://openalex.org/keywords/joint-probability-distribution","display_name":"Joint probability distribution","score":0.5094330906867981},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4959505498409271},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.4705137610435486},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.4261627793312073},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.3183988332748413},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.29991593956947327},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.1869683861732483},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.15795567631721497}],"concepts":[{"id":"https://openalex.org/C17618745","wikidata":"https://www.wikidata.org/wiki/Q207509","display_name":"Copula (linguistics)","level":2,"score":0.8329477310180664},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6211632490158081},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.5559066534042358},{"id":"https://openalex.org/C50382708","wikidata":"https://www.wikidata.org/wiki/Q223218","display_name":"Proportional hazards model","level":2,"score":0.5368552207946777},{"id":"https://openalex.org/C18653775","wikidata":"https://www.wikidata.org/wiki/Q1333358","display_name":"Joint probability distribution","level":2,"score":0.5094330906867981},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4959505498409271},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.4705137610435486},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.4261627793312073},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3183988332748413},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.29991593956947327},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.1869683861732483},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.15795567631721497},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1080/24725854.2020.1802792","is_oa":false,"landing_page_url":"https://doi.org/10.1080/24725854.2020.1802792","pdf_url":null,"source":{"id":"https://openalex.org/S4210225672","display_name":"IISE Transactions","issn_l":"2472-5854","issn":["2472-5854","2472-5862"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IISE Transactions","raw_type":"journal-article"},{"id":"pmh:oai:RePEc:taf:uiiexx:v:53:y:2021:i:9:p:1023-1036","is_oa":false,"landing_page_url":"http://hdl.handle.net/10.1080/24725854.2020.1802792","pdf_url":null,"source":{"id":"https://openalex.org/S4306401271","display_name":"RePEc: Research Papers in Economics","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I77793887","host_organization_name":"Federal Reserve Bank of St. Louis","host_organization_lineage":["https://openalex.org/I77793887"],"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":"article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","score":0.4099999964237213,"id":"https://metadata.un.org/sdg/9"}],"awards":[{"id":"https://openalex.org/G7877260903","display_name":null,"funder_award_id":"1824761","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"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W844875727","https://openalex.org/W1593493925","https://openalex.org/W1941823481","https://openalex.org/W1978358551","https://openalex.org/W1990098426","https://openalex.org/W1994337132","https://openalex.org/W1996734268","https://openalex.org/W2005931737","https://openalex.org/W2006572147","https://openalex.org/W2015339214","https://openalex.org/W2021362925","https://openalex.org/W2024794570","https://openalex.org/W2036136582","https://openalex.org/W2038951488","https://openalex.org/W2050603670","https://openalex.org/W2072430699","https://openalex.org/W2072872252","https://openalex.org/W2101749456","https://openalex.org/W2102890221","https://openalex.org/W2112081648","https://openalex.org/W2136916987","https://openalex.org/W2141102754","https://openalex.org/W2159198570","https://openalex.org/W2221635007","https://openalex.org/W2327932205","https://openalex.org/W2401688495","https://openalex.org/W2487770199","https://openalex.org/W2516153342","https://openalex.org/W2530516905","https://openalex.org/W2590354878","https://openalex.org/W2770688198","https://openalex.org/W2794747717","https://openalex.org/W3147894994","https://openalex.org/W4229652772","https://openalex.org/W4255982610","https://openalex.org/W4293738259","https://openalex.org/W4300451521"],"related_works":["https://openalex.org/W2059340305","https://openalex.org/W4254184784","https://openalex.org/W2057512905","https://openalex.org/W2368545092","https://openalex.org/W4206608468","https://openalex.org/W2076608411","https://openalex.org/W2353733188","https://openalex.org/W3094338031","https://openalex.org/W2794166214","https://openalex.org/W2792469005"],"abstract_inverted_index":{"Recent":[0],"advances":[1],"in":[2,125,160],"information":[3],"and":[4,47,67,123,132,144],"communication":[5],"technology":[6],"are":[7,48,104,110],"enabling":[8],"availability":[9],"of":[10,22,37,42,45,52,69,87,94,99,135,163],"event":[11,71,165],"sequence":[12],"data":[13,26],"from":[14,27],"equipment":[15],"fleets":[16],"comprising":[17],"potentially":[18],"a":[19,28,62,145],"large":[20],"number":[21],"similar":[23],"units.":[24],"The":[25,77,96],"specific":[29],"unit":[30],"may":[31],"be":[32],"related":[33],"to":[34,82,91],"multiple":[35,130],"types":[36,44],"events,":[38,131],"such":[39,70,138],"as":[40,50,139],"occurrence":[41],"different":[43],"failures,":[46],"recorded":[49],"part":[51],"the":[53,84,100,107,127,152,156],"unit\u2019s":[54],"service":[55,75],"history.":[56],"In":[57],"this":[58],"article,":[59],"we":[60,149],"present":[61],"novel":[63],"method":[64,79,120,154],"for":[65,106],"modeling":[66,126],"prediction":[68,161],"sequences":[72],"using":[73],"fleet":[74],"records.":[76],"proposed":[78,153],"uses":[80],"copula":[81,108],"approximate":[83],"joint":[85],"distribution":[86],"time-to-event":[88,101],"variables":[89,102],"corresponding":[90],"each":[92],"type":[93],"event.":[95],"marginal":[97],"distributions":[98],"that":[103,151],"needed":[105],"function":[109],"obtained":[111],"through":[112],"Cox":[113,140],"Proportional":[114],"Hazard":[115],"(PH)":[116],"regression":[117,158],"models.":[118],"Our":[119],"is":[121],"flexible":[122],"efficient":[124],"relationships":[128],"among":[129],"overcomes":[133],"limitations":[134],"traditional":[136],"approaches,":[137],"PH.":[141],"With":[142],"simulations":[143],"real-world":[146],"case":[147],"study,":[148],"demonstrate":[150],"outperforms":[155],"base":[157],"model":[159],"accuracy":[162],"future":[164],"occurrences.":[166]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2022,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}