{"id":"https://openalex.org/W2068928060","doi":"https://doi.org/10.1198/004017002188618554","title":"An Algorithm for Constructing Orthogonal and Nearly-Orthogonal Arrays With Mixed Levels and Small Runs","display_name":"An Algorithm for Constructing Orthogonal and Nearly-Orthogonal Arrays With Mixed Levels and Small Runs","publication_year":2002,"publication_date":"2002-11-01","ids":{"openalex":"https://openalex.org/W2068928060","doi":"https://doi.org/10.1198/004017002188618554","mag":"2068928060"},"language":"en","primary_location":{"id":"doi:10.1198/004017002188618554","is_oa":false,"landing_page_url":"https://doi.org/10.1198/004017002188618554","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,"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":"Technometrics","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/A5088673556","display_name":"Hongquan Xu","orcid":"https://orcid.org/0000-0002-3502-0664"},"institutions":[{"id":"https://openalex.org/I161318765","display_name":"University of California, Los Angeles","ror":"https://ror.org/046rm7j60","country_code":"US","type":"education","lineage":["https://openalex.org/I161318765"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Hongquan Xu","raw_affiliation_strings":["Department of Statistics, University of California, Los Angeles, CA 90095","University of California\u2013Los Angeles"],"affiliations":[{"raw_affiliation_string":"Department of Statistics, University of California, Los Angeles, CA 90095","institution_ids":["https://openalex.org/I161318765"]},{"raw_affiliation_string":"University of California\u2013Los Angeles","institution_ids":["https://openalex.org/I161318765"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5088673556"],"corresponding_institution_ids":["https://openalex.org/I161318765"],"apc_list":null,"apc_paid":null,"fwci":7.4112,"has_fulltext":false,"cited_by_count":127,"citation_normalized_percentile":{"value":0.97348155,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":"44","issue":"4","first_page":"356","last_page":"368"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11798","display_name":"Optimal Experimental Design Methods","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"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/T11798","display_name":"Optimal Experimental Design Methods","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"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/T10848","display_name":"Advanced Multi-Objective Optimization Algorithms","score":0.9973999857902527,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11159","display_name":"Manufacturing Process and Optimization","score":0.9940000176429749,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/orthogonal-array","display_name":"Orthogonal array","score":0.8239202499389648},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.7011110782623291},{"id":"https://openalex.org/keywords/flexibility","display_name":"Flexibility (engineering)","score":0.5800491571426392},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.5110896229743958},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.5045908689498901},{"id":"https://openalex.org/keywords/empirical-orthogonal-functions","display_name":"Empirical orthogonal functions","score":0.49982333183288574},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4961119592189789},{"id":"https://openalex.org/keywords/simple","display_name":"Simple (philosophy)","score":0.4959793984889984},{"id":"https://openalex.org/keywords/orthogonal-transformation","display_name":"Orthogonal transformation","score":0.4230080246925354},{"id":"https://openalex.org/keywords/variety","display_name":"Variety (cybernetics)","score":0.41720446944236755},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.351443886756897},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.12512320280075073},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.12266528606414795},{"id":"https://openalex.org/keywords/taguchi-methods","display_name":"Taguchi methods","score":0.08799079060554504}],"concepts":[{"id":"https://openalex.org/C42632107","wikidata":"https://www.wikidata.org/wiki/Q2031860","display_name":"Orthogonal array","level":3,"score":0.8239202499389648},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.7011110782623291},{"id":"https://openalex.org/C2780598303","wikidata":"https://www.wikidata.org/wiki/Q65921492","display_name":"Flexibility (engineering)","level":2,"score":0.5800491571426392},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.5110896229743958},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.5045908689498901},{"id":"https://openalex.org/C13724139","wikidata":"https://www.wikidata.org/wiki/Q3042818","display_name":"Empirical orthogonal functions","level":2,"score":0.49982333183288574},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4961119592189789},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.4959793984889984},{"id":"https://openalex.org/C54940322","wikidata":"https://www.wikidata.org/wiki/Q3997740","display_name":"Orthogonal transformation","level":2,"score":0.4230080246925354},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.41720446944236755},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.351443886756897},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.12512320280075073},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.12266528606414795},{"id":"https://openalex.org/C83469408","wikidata":"https://www.wikidata.org/wiki/Q2036525","display_name":"Taguchi methods","level":2,"score":0.08799079060554504},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0}],"mesh":[],"locations_count":5,"locations":[{"id":"doi:10.1198/004017002188618554","is_oa":false,"landing_page_url":"https://doi.org/10.1198/004017002188618554","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,"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":"Technometrics","raw_type":"journal-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.118.5124","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.118.5124","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.stat.ucla.edu/~hqxu/pub/Xu2002.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.15.8104","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.15.8104","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://pascal.stat.ucla.edu:16080/~hqxu/pub/tech-00-101.pdf","raw_type":"text"},{"id":"pmh:oai:escholarship.org/ark:/13030/qt1tg0s6nq","is_oa":false,"landing_page_url":"https://escholarship.org/uc/item/1tg0s6nq","pdf_url":null,"source":{"id":"https://openalex.org/S4306400115","display_name":"eScholarship (California Digital Library)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I2801248553","host_organization_name":"California Digital Library","host_organization_lineage":["https://openalex.org/I2801248553"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"article"},{"id":"pmh:qt1tg0s6nq","is_oa":false,"landing_page_url":"http://www.escholarship.org/uc/item/1tg0s6nq","pdf_url":null,"source":{"id":"https://openalex.org/S4306400115","display_name":"eScholarship (California Digital Library)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I2801248553","host_organization_name":"California Digital Library","host_organization_lineage":["https://openalex.org/I2801248553"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Xu, Hongquan. (2000). An Algorithm for Constructing Orthogonal and Nearly Orthogonal Arrays with Mixed Levels and Small Runs. Department of Statistics, UCLA. UCLA: Department of Statistics, UCLA. Retrieved from: http://www.escholarship.org/uc/item/1tg0s6nq","raw_type":"article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/8","score":0.5600000023841858,"display_name":"Decent work and economic growth"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":41,"referenced_works":["https://openalex.org/W10031511","https://openalex.org/W651150315","https://openalex.org/W1497211613","https://openalex.org/W1579752717","https://openalex.org/W1606003127","https://openalex.org/W1967331680","https://openalex.org/W1998797952","https://openalex.org/W2003657827","https://openalex.org/W2004115473","https://openalex.org/W2007821952","https://openalex.org/W2008232575","https://openalex.org/W2008291228","https://openalex.org/W2008493979","https://openalex.org/W2023039737","https://openalex.org/W2024060531","https://openalex.org/W2034624211","https://openalex.org/W2044771513","https://openalex.org/W2052900991","https://openalex.org/W2053090661","https://openalex.org/W2053856600","https://openalex.org/W2079913584","https://openalex.org/W2081928222","https://openalex.org/W2085149753","https://openalex.org/W2090843421","https://openalex.org/W2091878742","https://openalex.org/W2095536505","https://openalex.org/W2111499032","https://openalex.org/W2117135012","https://openalex.org/W2130326919","https://openalex.org/W2152150600","https://openalex.org/W2171733202","https://openalex.org/W2283688205","https://openalex.org/W2466383110","https://openalex.org/W2519881875","https://openalex.org/W2904250082","https://openalex.org/W3023540311","https://openalex.org/W4234702997","https://openalex.org/W4236546113","https://openalex.org/W4245521572","https://openalex.org/W4249486410","https://openalex.org/W4255347505"],"related_works":["https://openalex.org/W648345115","https://openalex.org/W4283073095","https://openalex.org/W2126292228","https://openalex.org/W2582373791","https://openalex.org/W4210525673","https://openalex.org/W4232228426","https://openalex.org/W4246218487","https://openalex.org/W2060879653","https://openalex.org/W2380891747","https://openalex.org/W2370666208"],"abstract_inverted_index":{"Orthogonal":[0],"arrays":[1,26,36,66],"are":[2,27,44],"used":[3],"widely":[4],"in":[5],"manufacturing":[6],"and":[7,12,46,57,64],"high-technology":[8],"industries":[9],"for":[10,60],"quality":[11],"productivity":[13],"improvement":[14],"experiments.":[15],"For":[16],"reasons":[17],"of":[18,32,50,72],"run":[19],"size":[20],"economy":[21],"or":[22,34],"\ufffd":[23,80],"exibility,":[24],"nearly-orthogonal":[25,35,65],"also":[28],"used.":[29],"The":[30],"construction":[31],"orthogonal":[33,63],"can":[37,68],"be":[38],"quite":[39],"challenging.":[40],"Most":[41],"existing":[42],"methods":[43],"complex":[45],"produce":[47],"limited":[48],"types":[49],"arrays.":[51],"This":[52],"article":[53],"describes":[54],"a":[55,70],"simple":[56],"effective":[58],"algorithm":[59],"constructing":[61],"mixed-level":[62],"that":[67],"construct":[69],"variety":[71],"small-run":[73],"designs":[74],"with":[75],"good":[76],"statistical":[77],"properties":[78],"ef":[79],"ciently.":[81],"KEY":[82],"WORDS:":[83],"D-optimality;":[84],"Exchange":[85],"algorithm;":[86,88],"Interchange":[87],"J":[89],"2-optimality.":[90],"1.":[91]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":7},{"year":2018,"cited_by_count":2},{"year":2017,"cited_by_count":4},{"year":2016,"cited_by_count":4},{"year":2015,"cited_by_count":6},{"year":2014,"cited_by_count":8},{"year":2013,"cited_by_count":6},{"year":2012,"cited_by_count":3}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}