{"id":"https://openalex.org/W3082087122","doi":"https://doi.org/10.1080/24725854.2020.1798038","title":"Modeling multivariate profiles using Gaussian process-controlled B-splines","display_name":"Modeling multivariate profiles using Gaussian process-controlled B-splines","publication_year":2020,"publication_date":"2020-08-28","ids":{"openalex":"https://openalex.org/W3082087122","doi":"https://doi.org/10.1080/24725854.2020.1798038","mag":"3082087122"},"language":"en","primary_location":{"id":"doi:10.1080/24725854.2020.1798038","is_oa":false,"landing_page_url":"https://doi.org/10.1080/24725854.2020.1798038","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/A5087379926","display_name":"Mithun Ghosh","orcid":"https://orcid.org/0000-0002-3599-4805"},"institutions":[{"id":"https://openalex.org/I138006243","display_name":"University of Arizona","ror":"https://ror.org/03m2x1q45","country_code":"US","type":"education","lineage":["https://openalex.org/I138006243"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mithun Ghosh","raw_affiliation_strings":["Department of Systems and Industrial Engineering, University of Arizona, Tucson, AZ, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Systems and Industrial Engineering, University of Arizona, Tucson, AZ, USA","institution_ids":["https://openalex.org/I138006243"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100373512","display_name":"Yongxiang Li","orcid":"https://orcid.org/0000-0001-8057-030X"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yongxiang Li","raw_affiliation_strings":["Department of Industrial Engineering and Management, Shanghai Jiao Tong University, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Industrial Engineering and Management, Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101801551","display_name":"Li Zeng","orcid":"https://orcid.org/0000-0003-3503-9135"},"institutions":[{"id":"https://openalex.org/I91045830","display_name":"Texas A&M University","ror":"https://ror.org/01f5ytq51","country_code":"US","type":"education","lineage":["https://openalex.org/I91045830"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Li Zeng","raw_affiliation_strings":["Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX, USA","institution_ids":["https://openalex.org/I91045830"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100764713","display_name":"Zijun Zhang","orcid":"https://orcid.org/0000-0002-2717-5033"},"institutions":[{"id":"https://openalex.org/I168719708","display_name":"City University of Hong Kong","ror":"https://ror.org/03q8dnn23","country_code":"HK","type":"education","lineage":["https://openalex.org/I168719708"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Zijun Zhang","raw_affiliation_strings":["School of Data Science, City University of Hong Kong, Kowloon Tong, Hong Kong"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Data Science, City University of Hong Kong, Kowloon Tong, Hong Kong","institution_ids":["https://openalex.org/I168719708"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101929509","display_name":"Qiang Zhou","orcid":"https://orcid.org/0000-0003-1348-8861"},"institutions":[{"id":"https://openalex.org/I138006243","display_name":"University of Arizona","ror":"https://ror.org/03m2x1q45","country_code":"US","type":"education","lineage":["https://openalex.org/I138006243"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Qiang Zhou","raw_affiliation_strings":["Department of Systems and Industrial Engineering, University of Arizona, Tucson, AZ, USA"],"raw_orcid":"https://orcid.org/0000-0003-1348-8861","affiliations":[{"raw_affiliation_string":"Department of Systems and Industrial Engineering, University of Arizona, Tucson, AZ, USA","institution_ids":["https://openalex.org/I138006243"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5101929509"],"corresponding_institution_ids":["https://openalex.org/I138006243"],"apc_list":null,"apc_paid":null,"fwci":2.6099,"has_fulltext":false,"cited_by_count":28,"citation_normalized_percentile":{"value":0.91865016,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":100},"biblio":{"volume":"53","issue":"7","first_page":"787","last_page":"798"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11443","display_name":"Advanced Statistical Process Monitoring","score":0.996999979019165,"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":0.996999979019165,"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/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9944000244140625,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing 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/T11583","display_name":"Advanced Measurement and Metrology Techniques","score":0.991599977016449,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.6819692254066467},{"id":"https://openalex.org/keywords/univariate","display_name":"Univariate","score":0.6697632074356079},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.5400948524475098},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5120556950569153},{"id":"https://openalex.org/keywords/flexibility","display_name":"Flexibility (engineering)","score":0.48220527172088623},{"id":"https://openalex.org/keywords/statistical-process-control","display_name":"Statistical process control","score":0.4790160059928894},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.4657593071460724},{"id":"https://openalex.org/keywords/mixed-model","display_name":"Mixed model","score":0.44663000106811523},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.430630624294281},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.4105421006679535},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3671204745769501},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.33507925271987915},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.31482312083244324},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.2901628613471985},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2220248281955719}],"concepts":[{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.6819692254066467},{"id":"https://openalex.org/C199163554","wikidata":"https://www.wikidata.org/wiki/Q1681619","display_name":"Univariate","level":3,"score":0.6697632074356079},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.5400948524475098},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5120556950569153},{"id":"https://openalex.org/C2780598303","wikidata":"https://www.wikidata.org/wiki/Q65921492","display_name":"Flexibility (engineering)","level":2,"score":0.48220527172088623},{"id":"https://openalex.org/C113644684","wikidata":"https://www.wikidata.org/wiki/Q1356717","display_name":"Statistical process control","level":3,"score":0.4790160059928894},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.4657593071460724},{"id":"https://openalex.org/C16012445","wikidata":"https://www.wikidata.org/wiki/Q1501135","display_name":"Mixed model","level":2,"score":0.44663000106811523},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.430630624294281},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.4105421006679535},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3671204745769501},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.33507925271987915},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.31482312083244324},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2901628613471985},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2220248281955719},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"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/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1080/24725854.2020.1798038","is_oa":false,"landing_page_url":"https://doi.org/10.1080/24725854.2020.1798038","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:7:p:787-798","is_oa":false,"landing_page_url":"http://hdl.handle.net/10.1080/24725854.2020.1798038","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":[{"score":0.7300000190734863,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":38,"referenced_works":["https://openalex.org/W21376189","https://openalex.org/W1423766661","https://openalex.org/W1479584399","https://openalex.org/W1510444525","https://openalex.org/W1511738908","https://openalex.org/W1524392826","https://openalex.org/W1534349507","https://openalex.org/W1580673759","https://openalex.org/W1607546201","https://openalex.org/W1964490787","https://openalex.org/W1991071089","https://openalex.org/W2002113995","https://openalex.org/W2004249081","https://openalex.org/W2026487916","https://openalex.org/W2026757872","https://openalex.org/W2030057222","https://openalex.org/W2045728125","https://openalex.org/W2062004166","https://openalex.org/W2066397388","https://openalex.org/W2088502517","https://openalex.org/W2090092366","https://openalex.org/W2094009285","https://openalex.org/W2102201073","https://openalex.org/W2131969788","https://openalex.org/W2139356790","https://openalex.org/W2157571709","https://openalex.org/W2244082754","https://openalex.org/W2531559099","https://openalex.org/W2597248468","https://openalex.org/W2597289420","https://openalex.org/W2808317395","https://openalex.org/W2906722476","https://openalex.org/W2938926128","https://openalex.org/W2946760180","https://openalex.org/W2963923468","https://openalex.org/W2977620188","https://openalex.org/W4212774754","https://openalex.org/W6607574207"],"related_works":["https://openalex.org/W2049578243","https://openalex.org/W1828158523","https://openalex.org/W2122079181","https://openalex.org/W2000145235","https://openalex.org/W1985848810","https://openalex.org/W2889939530","https://openalex.org/W2399195672","https://openalex.org/W3121881699","https://openalex.org/W2748838164","https://openalex.org/W2066015000"],"abstract_inverted_index":{"Due":[0],"to":[1,32,83,111,127],"the":[2,16,34,39,85,89,113,119,122,128,132],"increasing":[3],"presence":[4],"of":[5,15,27,38,88],"profile":[6,10,28,35,45],"data":[7],"in":[8,21],"manufacturing,":[9],"monitoring":[11,29],"has":[12],"become":[13],"one":[14],"most":[17],"popular":[18],"research":[19],"directions":[20],"statistical":[22],"process":[23],"control.":[24],"The":[25],"core":[26],"is":[30,51,61,125],"how":[31],"model":[33,59,93,114,124],"data.":[36],"Most":[37],"current":[40],"methods":[41],"deal":[42],"with":[43,65,115],"univariate":[44],"modeling":[46],"where":[47],"only":[48],"within-profile":[49],"correlation":[50],"considered.":[52],"In":[53,118],"this":[54],"article,":[55],"a":[56,102],"linear":[57,90],"mixed-effect":[58],"framework":[60],"adopted":[62],"for":[63],"dealing":[64],"multivariate":[66,103],"profiles,":[67],"having":[68],"both":[69],"within-":[70],"and":[71],"between-profile":[72],"correlations.":[73],"For":[74],"better":[75],"flexibility":[76],"yet":[77],"reduced":[78],"computational":[79],"cost,":[80],"we":[81],"propose":[82],"construct":[84],"random":[86],"component":[87],"mixed":[91],"effects":[92],"using":[94],"B-splines,":[95],"whose":[96],"control":[97],"points":[98],"are":[99],"governed":[100],"by":[101],"Gaussian":[104],"process.":[105],"Extensive":[106],"simulations":[107],"have":[108],"been":[109],"conducted":[110],"compare":[112],"classic":[116],"models.":[117],"case":[120],"study,":[121],"proposed":[123],"applied":[126],"transmittance":[129],"profiles":[130],"from":[131],"low-emittance":[133],"glasses.":[134]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":10},{"year":2021,"cited_by_count":3}],"updated_date":"2026-05-15T08:27:34.491423","created_date":"2025-10-10T00:00:00"}