{"id":"https://openalex.org/W311503734","doi":"https://doi.org/10.1198/004017004000000310","title":"Optimal Projective Three-Level Designs for Factor Screening and Interaction Detection","display_name":"Optimal Projective Three-Level Designs for Factor Screening and Interaction Detection","publication_year":2004,"publication_date":"2004-07-13","ids":{"openalex":"https://openalex.org/W311503734","doi":"https://doi.org/10.1198/004017004000000310","mag":"311503734"},"language":"en","primary_location":{"id":"doi:10.1198/004017004000000310","is_oa":false,"landing_page_url":"https://doi.org/10.1198/004017004000000310","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/A5035989329","display_name":"Hong Xu","orcid":"https://orcid.org/0000-0001-5461-0697"},"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":false,"raw_author_name":"Hongquan Xu","raw_affiliation_strings":["Department of Statistics, University of California, Los Angeles, CA 90095-1554"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics, University of California, Los Angeles, CA 90095-1554","institution_ids":["https://openalex.org/I161318765"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109438008","display_name":"Shao-Wei Cheng","orcid":"https://orcid.org/0000-0002-9735-2701"},"institutions":[{"id":"https://openalex.org/I4210141710","display_name":"Institute of Statistical Science, Academia Sinica","ror":"https://ror.org/044gv5910","country_code":"TW","type":"facility","lineage":["https://openalex.org/I4210141710","https://openalex.org/I84653119"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Shao-Wei Cheng","raw_affiliation_strings":["Institute of Statistical Science, Academia Sinica, Taipei 115, Taiwan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Statistical Science, Academia Sinica, Taipei 115, Taiwan","institution_ids":["https://openalex.org/I4210141710"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5085265745","display_name":"Cfj Wu","orcid":null},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"C.F.J Wu","raw_affiliation_strings":["School of Industrial and Systems Engineering, Georgia Institute of TechnologyAtlanta, GA 30332-0205"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Industrial and Systems Engineering, Georgia Institute of TechnologyAtlanta, GA 30332-0205","institution_ids":["https://openalex.org/I130701444"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.03992236,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"46","issue":"3","first_page":"280","last_page":"292"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11798","display_name":"Optimal Experimental Design Methods","score":0.9995999932289124,"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":0.9995999932289124,"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":0.9921000003814697,"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":0.972599983215332,"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/permutation","display_name":"Permutation (music)","score":0.6694488525390625},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5787642002105713},{"id":"https://openalex.org/keywords/orthogonal-array","display_name":"Orthogonal array","score":0.569131076335907},{"id":"https://openalex.org/keywords/factor","display_name":"Factor (programming language)","score":0.53706294298172},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5077043771743774},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.4211997985839844},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.36672860383987427},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3552015423774719},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.23302337527275085},{"id":"https://openalex.org/keywords/taguchi-methods","display_name":"Taguchi methods","score":0.13459309935569763}],"concepts":[{"id":"https://openalex.org/C21308566","wikidata":"https://www.wikidata.org/wiki/Q7169365","display_name":"Permutation (music)","level":2,"score":0.6694488525390625},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5787642002105713},{"id":"https://openalex.org/C42632107","wikidata":"https://www.wikidata.org/wiki/Q2031860","display_name":"Orthogonal array","level":3,"score":0.569131076335907},{"id":"https://openalex.org/C2781039887","wikidata":"https://www.wikidata.org/wiki/Q1391724","display_name":"Factor (programming language)","level":2,"score":0.53706294298172},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5077043771743774},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.4211997985839844},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.36672860383987427},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3552015423774719},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.23302337527275085},{"id":"https://openalex.org/C83469408","wikidata":"https://www.wikidata.org/wiki/Q2036525","display_name":"Taguchi methods","level":2,"score":0.13459309935569763},{"id":"https://openalex.org/C24890656","wikidata":"https://www.wikidata.org/wiki/Q82811","display_name":"Acoustics","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},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":5,"locations":[{"id":"doi:10.1198/004017004000000310","is_oa":false,"landing_page_url":"https://doi.org/10.1198/004017004000000310","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:10.1.1.143.1054","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.143.1054","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/XuChengWu2004.pdf","raw_type":"text"},{"id":"pmh:oai:escholarship.org/ark:/13030/qt8mv0h112","is_oa":false,"landing_page_url":"https://escholarship.org/uc/item/8mv0h112","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:qt8mv0h112","is_oa":false,"landing_page_url":"http://www.escholarship.org/uc/item/8mv0h112","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, Hong Quan; Cheng, Shao-Wei; & Wu, CFJ. (2002). Optimal Projective Three-Level Designs for Factor Screening and Interaction Detection. Department of Statistics, UCLA. UCLA: Department of Statistics, UCLA. Retrieved from: http://www.escholarship.org/uc/item/8mv0h112","raw_type":"article"},{"id":"mag:311503734","is_oa":false,"landing_page_url":"https://escholarship.org/uc/item/8mv0h112.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306507726","display_name":"Department of Statistics, UCLA","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":"Department of Statistics, UCLA","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","score":0.4399999976158142,"id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W1497211613","https://openalex.org/W1560843434","https://openalex.org/W1606003127","https://openalex.org/W2001414552","https://openalex.org/W2035814874","https://openalex.org/W2053090661","https://openalex.org/W2068928060","https://openalex.org/W2090843421","https://openalex.org/W2091878742","https://openalex.org/W2140478462","https://openalex.org/W2987849413","https://openalex.org/W4229543499","https://openalex.org/W4229652543","https://openalex.org/W4233706029"],"related_works":["https://openalex.org/W2020310505","https://openalex.org/W339200855","https://openalex.org/W289337958","https://openalex.org/W2468602994","https://openalex.org/W2765718668","https://openalex.org/W1523683236","https://openalex.org/W1986907660","https://openalex.org/W1685468186","https://openalex.org/W2238633096","https://openalex.org/W179762615","https://openalex.org/W2130978601","https://openalex.org/W2573446038","https://openalex.org/W2466383110","https://openalex.org/W2183233771","https://openalex.org/W2615746522","https://openalex.org/W2953299100","https://openalex.org/W2092902758","https://openalex.org/W2086984109","https://openalex.org/W2558887336","https://openalex.org/W2080583431"],"abstract_inverted_index":{"Orthogonal":[0],"arrays":[1],"(OAs)":[2],"are":[3,88,97,112,134,139],"widely":[4],"used":[5,32,98],"in":[6,20],"industrial":[7],"experiments":[8,22],"for":[9,35,40,65,80,90,99,117],"factor":[10,66],"screening.":[11],"Suppose":[12],"that":[13],"only":[14,34],"a":[15,44,52,73],"few":[16],"of":[17,46,54,63,95,106],"the":[18,21,61,101,107],"factors":[19],"turn":[23],"out":[24],"to":[25,59,78,115],"be":[26,31],"important.":[27],"An":[28],"OA":[29],"can":[30],"not":[33],"screening":[36],"factors,":[37],"but":[38],"also":[39],"detecting":[41],"interactions":[42],"among":[43],"subset":[45],"active":[47],"factors.":[48],"In":[49],"this":[50],"article":[51],"set":[53],"optimality":[55],"criteria":[56],"is":[57,76],"proposed":[58,77,89],"assess":[60],"performance":[62],"designs":[64,120,133],"screening,":[67],"projection,":[68],"and":[69,72,84,103,123,129,136],"interaction":[70],"detection,":[71],"three-step":[74],"approach":[75],"search":[79,116],"optimal":[81],"designs.":[82,93,109],"Combinatorial":[83],"algorithmic":[85],"construction":[86],"methods":[87],"generating":[91],"new":[92],"Permutations":[94],"levels":[96],"improving":[100],"eligibility":[102],"estimation":[104],"efficiency":[105],"projected":[108],"The":[110],"techniques":[111],"then":[113],"applied":[114],"best":[118],"three-level":[119],"with":[121],"18":[122],"27":[124],"runs.":[125],"Many":[126],"new,":[127],"efficient,":[128],"practically":[130],"useful":[131],"nonregular":[132],"found":[135],"their":[137],"properties":[138],"discussed.":[140]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}