{"id":"https://openalex.org/W2129249398","doi":"https://doi.org/10.1080/00401706.1989.10488524","title":"Robust Regression and Outlier Detection","display_name":"Robust Regression and Outlier Detection","publication_year":1989,"publication_date":"1989-05-01","ids":{"openalex":"https://openalex.org/W2129249398","doi":"https://doi.org/10.1080/00401706.1989.10488524","mag":"2129249398"},"language":"en","primary_location":{"id":"doi:10.1080/00401706.1989.10488524","doi":"https://doi.org/10.1080/00401706.1989.10488524","is_oa":false,"landing_page_url":"https://doi.org/10.1080/00401706.1989.10488524","pdf_url":null,"source":{"id":"https://openalex.org/S985303","display_name":"Technometrics","issn_l":"0040-1706","issn":["0040-1706","1537-2723"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["abdc-a","anvur-class-a","anvur-scientific","cwts-core","dongbi-c","doyens","jufo-2","norway-2","poland-140","russia-white-list-1","vabb-shw"],"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":"Technometrics","raw_type":"journal-article"},"parent_works":[],"lineage":["https://openalex.org/W2129249398"],"child_works_count":0,"type":"book-review","is_traditional":true,"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/A5022354463","display_name":"Gregory F. Piepel","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I1325736334","display_name":"Battelle","ror":"https://ror.org/01h5tnr73","country_code":"US","type":"nonprofit","lineage":["https://openalex.org/I1325736334"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Gregory F. Piepel","raw_affiliation_strings":["Battelle, Pacific Northwest Laboratories"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Battelle, Pacific Northwest Laboratories","institution_ids":["https://openalex.org/I1325736334"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075121395","display_name":"Peter J. Rousseeuw","orcid":"https://orcid.org/0000-0002-3807-5353","observed_orcids":["https://orcid.org/0000-0002-3807-5353"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Peter J. Rousseeuw","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5110095443","display_name":"Annick M. Leroy","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Annick M. Leroy","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5022354463"],"corresponding_institution_ids":["https://openalex.org/I1325736334"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":6159,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"31","issue":"2","first_page":"260","last_page":"260"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11871","display_name":"Advanced Statistical Methods and Models","score":1.0,"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":1.0,"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/T13487","display_name":"Statistical and numerical algorithms","score":0.0,"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"}},{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.0,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/robust-regression","display_name":"robust regression","score":1.0},{"id":"https://openalex.org/keywords/outlier-detection","display_name":"outlier detection","score":0.996999979019165},{"id":"https://openalex.org/keywords/simple-linear-regression","display_name":"simple linear regression","score":0.8629999756813049},{"id":"https://openalex.org/keywords/multiple-linear-regression","display_name":"multiple linear regression","score":0.878000020980835},{"id":"https://openalex.org/keywords/robust-algorithms","display_name":"robust algorithms","score":0.5690000057220459},{"id":"https://openalex.org/keywords/robust-estimation","display_name":"robust estimation","score":0.32899999618530273}],"concepts":[{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.8104091882705688},{"id":"https://openalex.org/C70259352","wikidata":"https://www.wikidata.org/wiki/Q1847839","display_name":"Robust regression","level":3,"score":0.6776708960533142},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.632377028465271},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.6104942560195923},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.5626449584960938},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.5619701743125916},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.48401352763175964},{"id":"https://openalex.org/C57381214","wikidata":"https://www.wikidata.org/wiki/Q55631393","display_name":"Regression diagnostic","level":4,"score":0.48079338669776917},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.46989795565605164},{"id":"https://openalex.org/C149769383","wikidata":"https://www.wikidata.org/wiki/Q7520804","display_name":"Simple linear regression","level":3,"score":0.4249560832977295},{"id":"https://openalex.org/C2777382242","wikidata":"https://www.wikidata.org/wiki/Q6017816","display_name":"Index (typography)","level":2,"score":0.4210319221019745},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.40476107597351074},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3898466229438782},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.32908087968826294},{"id":"https://openalex.org/C120068334","wikidata":"https://www.wikidata.org/wiki/Q45343","display_name":"Polynomial regression","level":3,"score":0.2447122037410736},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1080/00401706.1989.10488524","doi":"https://doi.org/10.1080/00401706.1989.10488524","is_oa":false,"landing_page_url":"https://doi.org/10.1080/00401706.1989.10488524","pdf_url":null,"source":{"id":"https://openalex.org/S985303","display_name":"Technometrics","issn_l":"0040-1706","issn":["0040-1706","1537-2723"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["abdc-a","anvur-class-a","anvur-scientific","cwts-core","dongbi-c","doyens","jufo-2","norway-2","poland-140","russia-white-list-1","vabb-shw"],"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":"Technometrics","raw_type":"journal-article"},{"id":"doi:10.2307/1268828","doi":"https://doi.org/10.2307/1268828","is_oa":false,"landing_page_url":"https://doi.org/10.2307/1268828","pdf_url":null,"source":{"id":"https://openalex.org/S985303","display_name":"Technometrics","issn_l":"0040-1706","issn":["0040-1706","1537-2723"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["abdc-a","anvur-class-a","anvur-scientific","cwts-core","dongbi-c","doyens","jufo-2","norway-2","poland-140","russia-white-list-1","vabb-shw"],"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":"Technometrics","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2333033681","https://openalex.org/W2103148824","https://openalex.org/W2991275755","https://openalex.org/W87261040","https://openalex.org/W1982616491","https://openalex.org/W2318597171","https://openalex.org/W2611281497","https://openalex.org/W2898294511","https://openalex.org/W2048430744","https://openalex.org/W96748704"],"abstract_inverted_index":{"1.":[0],"Introduction.":[1],"2.":[2],"Simple":[3],"Regression.":[4,7],"3.":[5],"Multiple":[6],"4.":[8],"The":[9],"Special":[10],"Case":[11],"of":[12,26],"One-Dimensional":[13],"Location.":[14],"5.":[15],"Algorithms.":[16],"6.":[17],"Outlier":[18],"Diagnostics.":[19],"7.":[20],"Related":[21],"Statistical":[22],"Techniques.":[23],"References.":[24],"Table":[25],"Data":[27],"Sets.":[28],"Index.":[29]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":44},{"year":2024,"cited_by_count":25},{"year":2023,"cited_by_count":28},{"year":2022,"cited_by_count":35},{"year":2021,"cited_by_count":164},{"year":2020,"cited_by_count":196},{"year":2019,"cited_by_count":168},{"year":2018,"cited_by_count":214},{"year":2017,"cited_by_count":229},{"year":2016,"cited_by_count":233},{"year":2015,"cited_by_count":299},{"year":2014,"cited_by_count":309},{"year":2013,"cited_by_count":285},{"year":2012,"cited_by_count":284}],"updated_date":"2026-11-14T11:04:58.165337","created_date":"2016-06-24T00:00:00"}