{"id":"https://openalex.org/W2908413686","doi":"https://doi.org/10.1080/03610918.2018.1547399","title":"Regression prediction method that is based on the partial errors-in-variables model","display_name":"Regression prediction method that is based on the partial errors-in-variables model","publication_year":2018,"publication_date":"2018-12-30","ids":{"openalex":"https://openalex.org/W2908413686","doi":"https://doi.org/10.1080/03610918.2018.1547399","mag":"2908413686"},"language":"en","primary_location":{"id":"doi:10.1080/03610918.2018.1547399","is_oa":false,"landing_page_url":"https://doi.org/10.1080/03610918.2018.1547399","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","https://openalex.org/P4310320449"],"host_organization_lineage_names":["Taylor & Francis","Informa"],"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/A5005395267","display_name":"Leyang Wang","orcid":"https://orcid.org/0000-0001-7919-2030"},"institutions":[{"id":"https://openalex.org/I4210119674","display_name":"East China University of Technology","ror":"https://ror.org/027385r44","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210119674"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Leyang Wang","raw_affiliation_strings":["Faculty of Geomatics, East China University of Technology, Nanchang, China;","Key Laboratory for Digital Land and Resources of Jiangxi Province, Nanchang, China","Key Laboratory of Watershed Ecology and Geographical Environment Monitoring, NASG, Nanchang, China;"],"raw_orcid":"https://orcid.org/0000-0001-7919-2030","affiliations":[{"raw_affiliation_string":"Faculty of Geomatics, East China University of Technology, Nanchang, China;","institution_ids":["https://openalex.org/I4210119674"]},{"raw_affiliation_string":"Key Laboratory for Digital Land and Resources of Jiangxi Province, Nanchang, China","institution_ids":[]},{"raw_affiliation_string":"Key Laboratory of Watershed Ecology and Geographical Environment Monitoring, NASG, Nanchang, China;","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5073322215","display_name":"Jianqiang Sun","orcid":"https://orcid.org/0000-0001-8619-0658"},"institutions":[{"id":"https://openalex.org/I4210119674","display_name":"East China University of Technology","ror":"https://ror.org/027385r44","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210119674"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianqiang Sun","raw_affiliation_strings":["Faculty of Geomatics, East China University of Technology, Nanchang, China;","Key Laboratory of Watershed Ecology and Geographical Environment Monitoring, NASG, Nanchang, China;"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Geomatics, East China University of Technology, Nanchang, China;","institution_ids":["https://openalex.org/I4210119674"]},{"raw_affiliation_string":"Key Laboratory of Watershed Ecology and Geographical Environment Monitoring, NASG, Nanchang, China;","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5005395267"],"corresponding_institution_ids":["https://openalex.org/I4210119674"],"apc_list":null,"apc_paid":null,"fwci":0.3235,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.55504281,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"49","issue":"12","first_page":"3380","last_page":"3395"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13487","display_name":"Statistical and numerical algorithms","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/2604","display_name":"Applied Mathematics"},"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/T13487","display_name":"Statistical and numerical algorithms","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/2604","display_name":"Applied 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/partial-least-squares-regression","display_name":"Partial least squares regression","score":0.6164955496788025},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.5601370334625244},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.5024101734161377},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4813971519470215},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.4334390163421631},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.32181021571159363},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3134157061576843},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2671052813529968}],"concepts":[{"id":"https://openalex.org/C22354355","wikidata":"https://www.wikidata.org/wiki/Q422009","display_name":"Partial least squares regression","level":2,"score":0.6164955496788025},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.5601370334625244},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.5024101734161377},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4813971519470215},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.4334390163421631},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.32181021571159363},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3134157061576843},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2671052813529968}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1080/03610918.2018.1547399","is_oa":false,"landing_page_url":"https://doi.org/10.1080/03610918.2018.1547399","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","https://openalex.org/P4310320449"],"host_organization_lineage_names":["Taylor & Francis","Informa"],"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":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W1480191977","https://openalex.org/W1966908428","https://openalex.org/W1971585425","https://openalex.org/W1972065056","https://openalex.org/W1978968684","https://openalex.org/W2008229822","https://openalex.org/W2032082196","https://openalex.org/W2043103176","https://openalex.org/W2064431362","https://openalex.org/W2084423326","https://openalex.org/W2118349012","https://openalex.org/W2524630010","https://openalex.org/W2781847846","https://openalex.org/W2801899428","https://openalex.org/W2992573501","https://openalex.org/W3025955450","https://openalex.org/W3045698055","https://openalex.org/W3201423827"],"related_works":["https://openalex.org/W2192745683","https://openalex.org/W2394208570","https://openalex.org/W2429686810","https://openalex.org/W2371059786","https://openalex.org/W2021174884","https://openalex.org/W31220157","https://openalex.org/W2312753042","https://openalex.org/W4289356671","https://openalex.org/W2389155397","https://openalex.org/W2165884543"],"abstract_inverted_index":{"The":[0,68],"observation":[1,48],"errors":[2,49,92],"of":[3,50,58,83],"independent":[4,95],"variables":[5,96,102],"in":[6,12,80,93],"model":[7,40,71,77],"prediction":[8,21,29,114],"are":[9],"typically":[10],"neglected":[11],"traditional":[13],"regression":[14,28],"models,":[15],"which":[16,43],"leads":[17],"to":[18],"the":[19,35,47,59,81,91,94,99,104,108],"decreased":[20],"accuracy.":[22,115],"In":[23],"this":[24],"paper,":[25],"a":[26,66,75],"new":[27],"method":[30,110],"that":[31,55],"is":[32,41,62,72],"based":[33],"on":[34],"partial":[36,69,105],"errors-in-variables":[37],"(partial":[38],"EIV)":[39],"proposed,":[42],"takes":[44],"into":[45,74],"account":[46],"all":[51],"variables.":[52],"It":[53],"observes":[54],"each":[56],"element":[57],"coefficient":[60],"matrix":[61],"an":[63],"expression":[64],"or":[65],"function.":[67],"EIV":[70,106],"transformed":[73],"linear":[76],"and":[78],"constructed":[79],"form":[82],"indirect":[84],"adjustment":[85],"for":[86],"iterative":[87],"solution.":[88],"By":[89],"considering":[90],"when":[97],"predicting":[98],"corresponding":[100],"dependent":[101],"with":[103],"model,":[107],"proposed":[109],"can":[111],"achieve":[112],"higher":[113]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2026-08-15T07:11:24.734988","created_date":"2025-10-10T00:00:00"}