{"id":"https://openalex.org/W1989002912","doi":"https://doi.org/10.1109/isie.2013.6563843","title":"A modified partial robust M-regression to improve prediction performance for data with outliers","display_name":"A modified partial robust M-regression to improve prediction performance for data with outliers","publication_year":2013,"publication_date":"2013-05-01","ids":{"openalex":"https://openalex.org/W1989002912","doi":"https://doi.org/10.1109/isie.2013.6563843","mag":"1989002912"},"language":"en","primary_location":{"id":"doi:10.1109/isie.2013.6563843","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isie.2013.6563843","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2013 IEEE International Symposium on Industrial Electronics","raw_type":"proceedings-article"},"type":"conference-paper","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/A5069744156","display_name":"Shen Yin","orcid":"https://orcid.org/0000-0002-3802-9269"},"institutions":[{"id":"https://openalex.org/I204983213","display_name":"Harbin Institute of Technology","ror":"https://ror.org/01yqg2h08","country_code":"CN","type":"education","lineage":["https://openalex.org/I204983213"]},{"id":"https://openalex.org/I97009856","display_name":"Bohai University","ror":"https://ror.org/01kdzej58","country_code":"CN","type":"education","lineage":["https://openalex.org/I97009856"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shen Yin","raw_affiliation_strings":["Bohai University, Jinzhou, China","Harbin Institute of Technology, Harbin, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Bohai University, Jinzhou, China","institution_ids":["https://openalex.org/I97009856"]},{"raw_affiliation_string":"Harbin Institute of Technology, Harbin, China","institution_ids":["https://openalex.org/I204983213"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100451760","display_name":"Guang Wang","orcid":"https://orcid.org/0000-0002-8014-6119"},"institutions":[{"id":"https://openalex.org/I204983213","display_name":"Harbin Institute of Technology","ror":"https://ror.org/01yqg2h08","country_code":"CN","type":"education","lineage":["https://openalex.org/I204983213"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guang Wang","raw_affiliation_strings":["Harbin Institute of Technology, Harbin, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Harbin Institute of Technology, Harbin, China","institution_ids":["https://openalex.org/I204983213"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":5.2514,"has_fulltext":false,"cited_by_count":12,"citation_normalized_percentile":{"value":0.95424837,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"18","issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11871","display_name":"Advanced Statistical Methods and Models","score":0.9988999962806702,"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/T11871","display_name":"Advanced Statistical Methods and Models","score":0.9988999962806702,"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/T10876","display_name":"Fault Detection and Control Systems","score":0.9884999990463257,"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/T11443","display_name":"Advanced Statistical Process Monitoring","score":0.9846000075340271,"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/outlier","display_name":"Outlier","score":0.8597403764724731},{"id":"https://openalex.org/keywords/weighting","display_name":"Weighting","score":0.7675944566726685},{"id":"https://openalex.org/keywords/robust-regression","display_name":"Robust regression","score":0.7520186305046082},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.749397337436676},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6559911966323853},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.6363921761512756},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.5782290697097778},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.5110467672348022},{"id":"https://openalex.org/keywords/linear-regression","display_name":"Linear regression","score":0.5051982998847961},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4474489688873291},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3997857868671417},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3234139084815979},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2676948308944702},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.2563183009624481},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2373286485671997}],"concepts":[{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.8597403764724731},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.7675944566726685},{"id":"https://openalex.org/C70259352","wikidata":"https://www.wikidata.org/wiki/Q1847839","display_name":"Robust regression","level":3,"score":0.7520186305046082},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.749397337436676},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6559911966323853},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.6363921761512756},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.5782290697097778},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.5110467672348022},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.5051982998847961},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4474489688873291},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3997857868671417},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3234139084815979},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2676948308944702},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2563183009624481},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2373286485671997},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.0},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/isie.2013.6563843","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isie.2013.6563843","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2013 IEEE International Symposium on Industrial Electronics","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W1573050432","https://openalex.org/W1970972107","https://openalex.org/W1976251851","https://openalex.org/W2018201690","https://openalex.org/W2030654976","https://openalex.org/W2031348368","https://openalex.org/W2051674548","https://openalex.org/W2054904291","https://openalex.org/W2070784069","https://openalex.org/W2081663460","https://openalex.org/W2084373150","https://openalex.org/W2088855970","https://openalex.org/W2103711750","https://openalex.org/W2113418007","https://openalex.org/W2124792803","https://openalex.org/W2144767994","https://openalex.org/W2158863190","https://openalex.org/W6654880681","https://openalex.org/W6664253998"],"related_works":["https://openalex.org/W3121734683","https://openalex.org/W4210813465","https://openalex.org/W2085680114","https://openalex.org/W1600426151","https://openalex.org/W1833314573","https://openalex.org/W3121311879","https://openalex.org/W4380879348","https://openalex.org/W1974187127","https://openalex.org/W1539940077","https://openalex.org/W4311044804"],"abstract_inverted_index":{"This":[0],"paper":[1],"introduces":[2],"a":[3,98,104,109,144],"modified":[4,110],"partial":[5,111],"robust":[6,35,112],"M-regression":[7,113],"approach.":[8],"The":[9,29],"objective":[10],"of":[11,21,123,139],"the":[12,18,22,44,63,71,80,85,121,129,135],"new":[13,130],"approach":[14,114,131],"is":[15,32,40,117],"to":[16,42,62,68,77,119],"improve":[17],"prediction":[19,147],"accuracy":[20],"regression":[23,37,87],"model":[24,88],"for":[25,74],"data":[26],"containing":[27],"outliers.":[28,95],"original":[30],"PRM":[31,57],"an":[33],"efficient":[34],"linear":[36],"method":[38,102],"which":[39,83],"devoting":[41],"down-weighting":[43],"outliers":[45],"by":[46,93],"choosing":[47],"proper":[48],"weighting":[49],"scheme":[50],"with":[51],"relatively":[52],"less":[53],"computational":[54],"load.":[55],"Although":[56],"shows":[58,127],"superior":[59],"performance":[60,148],"compared":[61],"existing":[64],"approaches,":[65],"it":[66],"fails":[67],"make":[69],"all":[70],"residual":[72],"weights":[73],"outlier":[75],"coverage":[76],"zeros":[78],"within":[79],"iteration":[81],"steps,":[82],"indicates":[84],"calculated":[86],"may":[89],"be":[90],"still":[91],"affected":[92],"these":[94],"Based":[96],"on":[97],"novel":[99],"distance":[100],"measurement":[101],"and":[103,137],"corresponding":[105],"center":[106],"estimate":[107],"method,":[108],"called":[115],"mPRM":[116],"presented":[118],"overcome":[120],"drawback":[122],"PRM.":[124,150],"Simulation":[125],"study":[126],"that":[128],"not":[132],"only":[133],"inherits":[134],"robustness":[136],"efficiency":[138],"PRM,":[140],"but":[141],"also":[142],"has":[143],"more":[145],"accurate":[146],"than":[149]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":3},{"year":2017,"cited_by_count":2},{"year":2016,"cited_by_count":2},{"year":2015,"cited_by_count":1},{"year":2014,"cited_by_count":3}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}