{"id":"https://openalex.org/W2022132296","doi":"https://doi.org/10.1080/03610910701723930","title":"Adjusted Confidence Bands in Nonparametric Regression","display_name":"Adjusted Confidence Bands in Nonparametric Regression","publication_year":2007,"publication_date":"2007-12-31","ids":{"openalex":"https://openalex.org/W2022132296","doi":"https://doi.org/10.1080/03610910701723930","mag":"2022132296"},"language":"en","primary_location":{"id":"doi:10.1080/03610910701723930","is_oa":false,"landing_page_url":"https://doi.org/10.1080/03610910701723930","pdf_url":null,"source":{"id":"https://openalex.org/S153329750","display_name":"Communications in Statistics - Simulation and Computation","issn_l":"0361-0918","issn":["0361-0918","1532-4141"],"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":"Communications in Statistics - Simulation and Computation","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/A5101884420","display_name":"Guoyi Zhang","orcid":"https://orcid.org/0009-0003-9392-1086"},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Guoyi Zhang","raw_affiliation_strings":["Department of Mathematics and Statistics , Arizona State University , Tempe, Arizona, USA","Department of Mathematics and Statistics Arizona State University  Tempe Arizona USA"],"affiliations":[{"raw_affiliation_string":"Department of Mathematics and Statistics , Arizona State University , Tempe, Arizona, USA","institution_ids":["https://openalex.org/I55732556"]},{"raw_affiliation_string":"Department of Mathematics and Statistics Arizona State University  Tempe Arizona USA","institution_ids":["https://openalex.org/I55732556"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101995753","display_name":"Yan Lu","orcid":"https://orcid.org/0009-0000-5665-6306"},"institutions":[{"id":"https://openalex.org/I169521973","display_name":"University of New Mexico","ror":"https://ror.org/05fs6jp91","country_code":"US","type":"education","lineage":["https://openalex.org/I169521973"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yan Lu","raw_affiliation_strings":["Department of Mathematics and Statistics , University of New Mexico , Albuquerque, New Mexico, USA","Department of Mathematics and Statistics, University of New Mexico, Albuquerque, New Mexico, USA"],"affiliations":[{"raw_affiliation_string":"Department of Mathematics and Statistics , University of New Mexico , Albuquerque, New Mexico, USA","institution_ids":["https://openalex.org/I169521973"]},{"raw_affiliation_string":"Department of Mathematics and Statistics, University of New Mexico, Albuquerque, New Mexico, USA","institution_ids":["https://openalex.org/I169521973"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5101884420"],"corresponding_institution_ids":["https://openalex.org/I55732556"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.138825,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"37","issue":"1","first_page":"106","last_page":"113"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11798","display_name":"Optimal Experimental Design Methods","score":0.9936000108718872,"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":0.9936000108718872,"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/T10928","display_name":"Probabilistic and Robust Engineering Design","score":0.9914000034332275,"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"}},{"id":"https://openalex.org/T11871","display_name":"Advanced Statistical Methods and Models","score":0.9843000173568726,"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/confidence-and-prediction-bands","display_name":"Confidence and prediction bands","score":0.7646982669830322},{"id":"https://openalex.org/keywords/confidence-interval","display_name":"Confidence interval","score":0.650611400604248},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.6388649940490723},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.5611283779144287},{"id":"https://openalex.org/keywords/logistic-regression","display_name":"Logistic regression","score":0.5565652847290039},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.5540138483047485},{"id":"https://openalex.org/keywords/nonparametric-statistics","display_name":"Nonparametric statistics","score":0.5214030742645264},{"id":"https://openalex.org/keywords/nonparametric-regression","display_name":"Nonparametric regression","score":0.5076309442520142},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.46563687920570374},{"id":"https://openalex.org/keywords/linear-regression","display_name":"Linear regression","score":0.4326660931110382},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4278404712677002},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3796049952507019}],"concepts":[{"id":"https://openalex.org/C140529851","wikidata":"https://www.wikidata.org/wiki/Q5160083","display_name":"Confidence and prediction bands","level":3,"score":0.7646982669830322},{"id":"https://openalex.org/C44249647","wikidata":"https://www.wikidata.org/wiki/Q208498","display_name":"Confidence interval","level":2,"score":0.650611400604248},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.6388649940490723},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.5611283779144287},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.5565652847290039},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.5540138483047485},{"id":"https://openalex.org/C102366305","wikidata":"https://www.wikidata.org/wiki/Q1097688","display_name":"Nonparametric statistics","level":2,"score":0.5214030742645264},{"id":"https://openalex.org/C74127309","wikidata":"https://www.wikidata.org/wiki/Q3455886","display_name":"Nonparametric regression","level":3,"score":0.5076309442520142},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.46563687920570374},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.4326660931110382},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4278404712677002},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3796049952507019}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1080/03610910701723930","is_oa":false,"landing_page_url":"https://doi.org/10.1080/03610910701723930","pdf_url":null,"source":{"id":"https://openalex.org/S153329750","display_name":"Communications in Statistics - Simulation and Computation","issn_l":"0361-0918","issn":["0361-0918","1532-4141"],"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":"Communications in Statistics - Simulation and Computation","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":11,"referenced_works":["https://openalex.org/W1968391698","https://openalex.org/W1985905466","https://openalex.org/W2008353574","https://openalex.org/W2021438475","https://openalex.org/W2041472063","https://openalex.org/W2075616915","https://openalex.org/W2082965617","https://openalex.org/W2093189858","https://openalex.org/W2144494458","https://openalex.org/W2145850868","https://openalex.org/W4248064012"],"related_works":["https://openalex.org/W2184572292","https://openalex.org/W1541412963","https://openalex.org/W4321367829","https://openalex.org/W1509119367","https://openalex.org/W4309301076","https://openalex.org/W2041704562","https://openalex.org/W2000388799","https://openalex.org/W2288767749","https://openalex.org/W920448194","https://openalex.org/W3151983414"],"abstract_inverted_index":{"Suppose":[0],"we":[1],"have":[2,41],"{(x":[3],"i":[4,7,9],",":[5],"y":[6],")}":[8],"=":[10],"1,":[11],"2,\u2026,":[12],"n,":[13],"a":[14,74,139],"sequence":[15],"of":[16,60,67,102,116],"independent":[17],"observations.":[18],"We":[19],"wish":[20],"to":[21,53,86,96,125,131],"find":[22],"approximate":[23],"1":[24],"\u2212":[25],"\u03b1":[26],"simultaneous":[27],"confidence":[28,36,69,109],"bands":[29,37,70,89,110],"for":[30,90,107],"the":[31,39,47,55,61,88,98,103,117],"regression":[32,56,79],"curve.":[33,57],"Many":[34],"previous":[35],"in":[38,80],"literature":[40],"practical":[42],"difficulties.":[43],"In":[44],"this":[45],"article,":[46],"local":[48],"linear":[49],"smoother":[50],"is":[51,63,84,111,120,123],"used":[52,95],"estimate":[54],"The":[58,105,114],"bias":[59],"estimator":[62],"considered.":[64],"Different":[65],"methods":[66],"constructing":[68,108],"are":[71,94],"discussed.":[72],"Finally,":[73],"possible":[75],"method":[76,119],"incorporating":[77],"logistic":[78],"an":[81],"innovative":[82],"way":[83],"proposed":[85,118],"construct":[87],"random":[91,132],"designs.":[92,133],"Simulations":[93],"study":[97],"performance":[99],"or":[100],"properties":[101],"methods.":[104],"procedure":[106],"entirely":[112],"data-driven.":[113],"advantage":[115],"that":[121],"it":[122],"simple":[124],"use":[126],"and":[127,142],"can":[128,135],"be":[129,136],"applied":[130],"It":[134],"considered":[137],"as":[138],"practically":[140],"useful":[141],"efficient":[143],"method.":[144]},"counts_by_year":[{"year":2014,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
