{"id":"https://openalex.org/W1690572014","doi":"https://doi.org/10.1198/00401700152672483","title":"Methods for Identifying Dispersion Effects in Unreplicated Factorial Experiments","display_name":"Methods for Identifying Dispersion Effects in Unreplicated Factorial Experiments","publication_year":2001,"publication_date":"2001-11-01","ids":{"openalex":"https://openalex.org/W1690572014","doi":"https://doi.org/10.1198/00401700152672483","mag":"1690572014"},"language":"en","primary_location":{"id":"doi:10.1198/00401700152672483","is_oa":false,"landing_page_url":"https://doi.org/10.1198/00401700152672483","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/A5003771469","display_name":"William A. Brenneman","orcid":null},"institutions":[{"id":"https://openalex.org/I4210108292","display_name":"Procter & Gamble (Netherlands)","ror":"https://ror.org/01sf6dn24","country_code":"NL","type":"company","lineage":["https://openalex.org/I4210108292","https://openalex.org/I74680897"]},{"id":"https://openalex.org/I74680897","display_name":"Procter & Gamble (United States)","ror":"https://ror.org/04dkns738","country_code":"US","type":"company","lineage":["https://openalex.org/I74680897"]}],"countries":["NL","US"],"is_corresponding":false,"raw_author_name":"William A Brenneman","raw_affiliation_strings":["Biometrics and Statistical Sciences Department, Health Care Research Center, The Procter & Gamble Company, Mason, OH 45040","Procter & Gamble"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Biometrics and Statistical Sciences Department, Health Care Research Center, The Procter & Gamble Company, Mason, OH 45040","institution_ids":["https://openalex.org/I74680897"]},{"raw_affiliation_string":"Procter & Gamble","institution_ids":["https://openalex.org/I4210108292"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5078363534","display_name":"Vijayan N. Nair","orcid":"https://orcid.org/0000-0002-6511-7432"},"institutions":[{"id":"https://openalex.org/I27837315","display_name":"University of Michigan","ror":"https://ror.org/00jmfr291","country_code":"US","type":"education","lineage":["https://openalex.org/I27837315"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Vijayan N Nair","raw_affiliation_strings":["Department of Statistics and Department of Industrial & Operations Engineering, The University of Michigan, Ann Arbor, MI 48109-1285","Statistics"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics and Department of Industrial & Operations Engineering, The University of Michigan, Ann Arbor, MI 48109-1285","institution_ids":["https://openalex.org/I27837315"]},{"raw_affiliation_string":"Statistics","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":3.2235,"has_fulltext":false,"cited_by_count":44,"citation_normalized_percentile":{"value":0.91211381,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"43","issue":"4","first_page":"388","last_page":"405"},"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/T11235","display_name":"Statistical Methods in Clinical Trials","score":0.9904999732971191,"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/T11871","display_name":"Advanced Statistical Methods and Models","score":0.9854999780654907,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/dispersion","display_name":"Dispersion (optics)","score":0.7932839393615723},{"id":"https://openalex.org/keywords/factorial-experiment","display_name":"Factorial experiment","score":0.5812346339225769},{"id":"https://openalex.org/keywords/fractional-factorial-design","display_name":"Fractional factorial design","score":0.5558797717094421},{"id":"https://openalex.org/keywords/factorial","display_name":"Factorial","score":0.49709799885749817},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.4939773678779602},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4865650236606598},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.48359817266464233},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.4717332422733307},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.41894787549972534},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.4158303141593933},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.33681294322013855},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.13097473978996277},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.05959230661392212}],"concepts":[{"id":"https://openalex.org/C177562468","wikidata":"https://www.wikidata.org/wiki/Q182893","display_name":"Dispersion (optics)","level":2,"score":0.7932839393615723},{"id":"https://openalex.org/C169222746","wikidata":"https://www.wikidata.org/wiki/Q4116558","display_name":"Factorial experiment","level":2,"score":0.5812346339225769},{"id":"https://openalex.org/C16469947","wikidata":"https://www.wikidata.org/wiki/Q2400745","display_name":"Fractional factorial design","level":3,"score":0.5558797717094421},{"id":"https://openalex.org/C183763347","wikidata":"https://www.wikidata.org/wiki/Q120976","display_name":"Factorial","level":2,"score":0.49709799885749817},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.4939773678779602},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4865650236606598},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.48359817266464233},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.4717332422733307},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.41894787549972534},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.4158303141593933},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.33681294322013855},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.13097473978996277},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.05959230661392212},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1198/00401700152672483","is_oa":false,"landing_page_url":"https://doi.org/10.1198/00401700152672483","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"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W58266293","https://openalex.org/W1528905581","https://openalex.org/W1966932096","https://openalex.org/W1997977011","https://openalex.org/W2002358704","https://openalex.org/W2011905386","https://openalex.org/W2035983506","https://openalex.org/W2049481249","https://openalex.org/W2053020350","https://openalex.org/W2058404065","https://openalex.org/W2060135116","https://openalex.org/W2066771629","https://openalex.org/W2085289178","https://openalex.org/W2086244482","https://openalex.org/W2104167971","https://openalex.org/W2135959738","https://openalex.org/W2147530768","https://openalex.org/W2169064458","https://openalex.org/W2185888558","https://openalex.org/W2329042579","https://openalex.org/W2413573154","https://openalex.org/W2618832586","https://openalex.org/W2798196259","https://openalex.org/W4213141362","https://openalex.org/W4230549143","https://openalex.org/W4242428934","https://openalex.org/W4256394954","https://openalex.org/W4256481133"],"related_works":["https://openalex.org/W4297784416","https://openalex.org/W1590352712","https://openalex.org/W1988781812","https://openalex.org/W4230472231","https://openalex.org/W2011415552","https://openalex.org/W4254259809","https://openalex.org/W4230378348","https://openalex.org/W1554048027","https://openalex.org/W4229861962","https://openalex.org/W2480473412"],"abstract_inverted_index":{"There":[0],"has":[1],"been":[2,71],"considerable":[3],"interest":[4],"recently":[5],"in":[6],"the":[7,83,99,105,127,141],"use":[8],"of":[9,24,43,62,92,126],"statistically":[10],"designed":[11],"experiments":[12],"to":[13,81,110,139],"identify":[14],"both":[15,44],"location":[16,45],"and":[17,46,76,124,133],"dispersion":[18,25,47,128],"effects":[19,26,48],"for":[20,121],"quality":[21],"improvement.":[22],"Analysis":[23],"usually":[27],"requires":[28],"replications":[29],"that":[30,65],"can":[31],"be":[32],"expensive":[33],"or":[34,69],"time":[35],"consuming.":[36],"Several":[37],"recent":[38],"articles":[39],"have":[40,70,95],"considered":[41],"identification":[42],"from":[49,89],"unreplicated":[50],"fractional":[51],"factorial":[52],"experiments.":[53],"In":[54],"this":[55],"article,":[56],"we":[57,116],"provide":[58],"a":[59],"systematic":[60],"study":[61],"various":[63],"methods":[64,87],"are":[66,79,137],"commonly":[67],"used":[68,80,138],"proposed":[72],"recently.":[73],"Both":[74],"theoretical":[75],"simulation":[77],"results":[78],"characterize":[82],"properties.":[84],"Although":[85],"all":[86],"suffer":[88],"some":[90,94,118],"degree":[91],"bias,":[93],"serious":[96],"problems":[97],"when":[98],"bias":[100],"remains":[101],"large":[102],"even":[103],"as":[104,135],"design":[106],"run":[107],"size":[108],"increases":[109],"infinity.":[111],"Based":[112],"on":[113],"these":[114],"analyses,":[115],"propose":[117],"iterative":[119],"strategies":[120],"model":[122],"selection":[123],"estimation":[125],"effects.":[129],"A":[130],"real":[131],"example":[132],"simulations":[134],"well":[136],"illustrate":[140],"results.":[142]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2019,"cited_by_count":2},{"year":2016,"cited_by_count":2},{"year":2015,"cited_by_count":4},{"year":2014,"cited_by_count":4},{"year":2013,"cited_by_count":4},{"year":2012,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
