{"id":"https://openalex.org/W2927206695","doi":"https://doi.org/10.1080/03610918.2019.1588312","title":"High-dimensional data monitoring using support machines","display_name":"High-dimensional data monitoring using support machines","publication_year":2019,"publication_date":"2019-04-03","ids":{"openalex":"https://openalex.org/W2927206695","doi":"https://doi.org/10.1080/03610918.2019.1588312","mag":"2927206695"},"language":"en","primary_location":{"id":"doi:10.1080/03610918.2019.1588312","is_oa":false,"landing_page_url":"https://doi.org/10.1080/03610918.2019.1588312","pdf_url":null,"source":{"id":"https://openalex.org/S153329750","display_name":"Communications in Statistics - Simulation and Computation","issn_l":"0361-0918","issn":["0361-0918","1532-4141"],"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":"Communications in Statistics - Simulation and Computation","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/A5006532483","display_name":"Edgard M. Maboudou\u2010Tchao","orcid":"https://orcid.org/0000-0001-6783-282X"},"institutions":[{"id":"https://openalex.org/I106165777","display_name":"University of Central Florida","ror":"https://ror.org/036nfer12","country_code":"US","type":"education","lineage":["https://openalex.org/I106165777"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Edgard M. Maboudou-Tchao","raw_affiliation_strings":["Department of Statistics, University of Central Florida, Orlando, FL, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics, University of Central Florida, Orlando, FL, USA","institution_ids":["https://openalex.org/I106165777"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5006532483"],"corresponding_institution_ids":["https://openalex.org/I106165777"],"apc_list":null,"apc_paid":null,"fwci":1.0012,"has_fulltext":false,"cited_by_count":19,"citation_normalized_percentile":{"value":0.78713736,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"50","issue":"7","first_page":"1927","last_page":"1942"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11443","display_name":"Advanced Statistical Process Monitoring","score":0.9987000226974487,"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"}},"topics":[{"id":"https://openalex.org/T11443","display_name":"Advanced Statistical Process Monitoring","score":0.9987000226974487,"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"}},{"id":"https://openalex.org/T10876","display_name":"Fault Detection and Control Systems","score":0.9894999861717224,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"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/T11871","display_name":"Advanced Statistical Methods and Models","score":0.9865000247955322,"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/control-chart","display_name":"Control chart","score":0.8717895746231079},{"id":"https://openalex.org/keywords/shewhart-individuals-control-chart","display_name":"Shewhart individuals control chart","score":0.7805633544921875},{"id":"https://openalex.org/keywords/statistical-process-control","display_name":"Statistical process control","score":0.6338733434677124},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6178310513496399},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.521103024482727},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.5116458535194397},{"id":"https://openalex.org/keywords/chart","display_name":"Chart","score":0.4608314633369446},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.4549325704574585},{"id":"https://openalex.org/keywords/type-i-and-type-ii-errors","display_name":"Type I and type II errors","score":0.44513049721717834},{"id":"https://openalex.org/keywords/ewma-chart","display_name":"EWMA chart","score":0.4424188733100891},{"id":"https://openalex.org/keywords/bar-x-and-r-chart","display_name":"\\bar x and R chart","score":0.43164315819740295},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.38556668162345886},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.26830026507377625},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2534520626068115}],"concepts":[{"id":"https://openalex.org/C196985124","wikidata":"https://www.wikidata.org/wiki/Q1369242","display_name":"Control chart","level":3,"score":0.8717895746231079},{"id":"https://openalex.org/C159848633","wikidata":"https://www.wikidata.org/wiki/Q7495725","display_name":"Shewhart individuals control chart","level":5,"score":0.7805633544921875},{"id":"https://openalex.org/C113644684","wikidata":"https://www.wikidata.org/wiki/Q1356717","display_name":"Statistical process control","level":3,"score":0.6338733434677124},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6178310513496399},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.521103024482727},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.5116458535194397},{"id":"https://openalex.org/C190812933","wikidata":"https://www.wikidata.org/wiki/Q28923","display_name":"Chart","level":2,"score":0.4608314633369446},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.4549325704574585},{"id":"https://openalex.org/C40696583","wikidata":"https://www.wikidata.org/wiki/Q989120","display_name":"Type I and type II errors","level":2,"score":0.44513049721717834},{"id":"https://openalex.org/C74746147","wikidata":"https://www.wikidata.org/wiki/Q5324652","display_name":"EWMA chart","level":4,"score":0.4424188733100891},{"id":"https://openalex.org/C129031352","wikidata":"https://www.wikidata.org/wiki/Q2597868","display_name":"\\bar x and R chart","level":5,"score":0.43164315819740295},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.38556668162345886},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.26830026507377625},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2534520626068115},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1080/03610918.2019.1588312","is_oa":false,"landing_page_url":"https://doi.org/10.1080/03610918.2019.1588312","pdf_url":null,"source":{"id":"https://openalex.org/S153329750","display_name":"Communications in Statistics - Simulation and Computation","issn_l":"0361-0918","issn":["0361-0918","1532-4141"],"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":"Communications in Statistics - Simulation and Computation","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/8","score":0.4099999964237213,"display_name":"Decent work and economic growth"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W1592078486","https://openalex.org/W1976990135","https://openalex.org/W1977106467","https://openalex.org/W1980171500","https://openalex.org/W2004096570","https://openalex.org/W2025750035","https://openalex.org/W2026526118","https://openalex.org/W2039813040","https://openalex.org/W2048500077","https://openalex.org/W2059128962","https://openalex.org/W2059290350","https://openalex.org/W2080795935","https://openalex.org/W2084812512","https://openalex.org/W2100294832","https://openalex.org/W2109211671","https://openalex.org/W2130827941","https://openalex.org/W2157175419","https://openalex.org/W2158142902","https://openalex.org/W2161088630","https://openalex.org/W2240082739","https://openalex.org/W2333124900","https://openalex.org/W2518969547","https://openalex.org/W2553608857","https://openalex.org/W2608431721","https://openalex.org/W2770867468"],"related_works":["https://openalex.org/W2931607628","https://openalex.org/W2921601162","https://openalex.org/W4381431108","https://openalex.org/W2037626511","https://openalex.org/W2228904849","https://openalex.org/W1544415648","https://openalex.org/W194400357","https://openalex.org/W2030022886","https://openalex.org/W2181580412","https://openalex.org/W19159520"],"abstract_inverted_index":{"Dealing":[0],"with":[1],"high-dimensional":[2,37,144],"data":[3],"is":[4,44,71,92],"a":[5,103,124,143],"major":[6],"challenge":[7],"primary":[8],"in":[9,13,30,113],"statistics":[10],"and":[11,122],"consequently":[12],"statistical":[14,129],"process":[15],"control":[16,21,84,90,135,163],"(SPC).":[17],"\u201cShewhart-type\u201d":[18],"charts":[19,22,27,64,75],"are":[20,28],"using":[23],"rational":[24,67],"subgrouping.":[25],"Shewhart-type":[26,63,74],"effective":[29],"the":[31,40,52,86,89,96,99,161],"detection":[32],"of":[33,42,54,88,150],"large":[34,47],"shifts.":[35],"In":[36],"settings,":[38],"where":[39],"number":[41,53],"variables":[43],"nearly":[45],"as":[46,48],"or":[49],"larger":[50],"than":[51],"observations,":[55],"it":[56],"will":[57],"be":[58,111],"very":[59,166],"hard":[60],"to":[61,72,141],"use":[62,73,149],"based":[65,76,94],"on":[66,77,95],"subgroups.":[68],"An":[69],"alternative":[70],"individual":[78],"observations.":[79],"Also,":[80],"for":[81],"most":[82],"conventional":[83],"charts,":[85],"design":[87],"limits":[91],"commonly":[93],"assumption":[97],"that":[98,160],"quality":[100],"characteristics":[101],"follow":[102],"multivariate":[104,134],"normal":[105],"distribution.":[106],"However,":[107],"this":[108],"may":[109],"not":[110],"reasonable":[112],"many":[114],"real-world":[115],"problems.":[116],"This":[117,146],"paper":[118],"addresses":[119],"these":[120],"issues":[121],"suggests":[123],"monitoring":[125],"methodology":[126],"motivated":[127],"by":[128],"learning":[130],"theory.":[131],"The":[132],"proposed":[133,162],"chart":[136,147,164],"uses":[137],"tensor":[138],"space":[139],"model":[140],"represent":[142],"vector.":[145],"makes":[148],"information":[151],"extracted":[152],"from":[153],"in-control":[154],"preliminary":[155],"samples.":[156],"Simulation":[157],"studies":[158],"demonstrate":[159],"has":[165],"good":[167],"performances.":[168]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":3},{"year":2019,"cited_by_count":1}],"updated_date":"2026-07-19T07:52:34.831488","created_date":"2025-10-10T00:00:00"}
