{"id":"https://openalex.org/W4210247028","doi":"https://doi.org/10.1080/10618600.2022.2037434","title":"Estimation and Model Selection for Nonparametric Function-on-Function Regression","display_name":"Estimation and Model Selection for Nonparametric Function-on-Function Regression","publication_year":2022,"publication_date":"2022-02-04","ids":{"openalex":"https://openalex.org/W4210247028","doi":"https://doi.org/10.1080/10618600.2022.2037434","pmid":"https://pubmed.ncbi.nlm.nih.gov/36594047"},"language":"en","primary_location":{"id":"doi:10.1080/10618600.2022.2037434","is_oa":false,"landing_page_url":"https://doi.org/10.1080/10618600.2022.2037434","pdf_url":null,"source":{"id":"https://openalex.org/S76159266","display_name":"Journal of Computational and Graphical Statistics","issn_l":"1061-8600","issn":["1061-8600","1537-2715"],"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":"Journal of Computational and Graphical Statistics","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/9802009","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101553162","display_name":"Zhanfeng Wang","orcid":"https://orcid.org/0000-0002-7545-1063"},"institutions":[{"id":"https://openalex.org/I126520041","display_name":"University of Science and Technology of China","ror":"https://ror.org/04c4dkn09","country_code":"CN","type":"education","lineage":["https://openalex.org/I126520041","https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhanfeng Wang","raw_affiliation_strings":["International Institute of Finance, The School of Management, University of Science and Technology of China, Hefei, China;"],"affiliations":[{"raw_affiliation_string":"International Institute of Finance, The School of Management, University of Science and Technology of China, Hefei, China;","institution_ids":["https://openalex.org/I126520041"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013384564","display_name":"Hao Dong","orcid":"https://orcid.org/0000-0002-8082-985X"},"institutions":[{"id":"https://openalex.org/I154570441","display_name":"University of California, Santa Barbara","ror":"https://ror.org/02t274463","country_code":"US","type":"education","lineage":["https://openalex.org/I154570441"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hao Dong","raw_affiliation_strings":["Department of Statistics and Applied Probability, University of California, Santa Barbara, Santa Barbara, CA;"],"affiliations":[{"raw_affiliation_string":"Department of Statistics and Applied Probability, University of California, Santa Barbara, Santa Barbara, CA;","institution_ids":["https://openalex.org/I154570441"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100664656","display_name":"Ping Ma","orcid":"https://orcid.org/0000-0002-5728-3596"},"institutions":[{"id":"https://openalex.org/I165733156","display_name":"University of Georgia","ror":"https://ror.org/00te3t702","country_code":"US","type":"education","lineage":["https://openalex.org/I165733156"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ping Ma","raw_affiliation_strings":["Department of Statistics, University of Georgia, Athens, GA"],"affiliations":[{"raw_affiliation_string":"Department of Statistics, University of Georgia, Athens, GA","institution_ids":["https://openalex.org/I165733156"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5036224267","display_name":"Yuedong Wang","orcid":"https://orcid.org/0000-0001-9202-6723"},"institutions":[{"id":"https://openalex.org/I154570441","display_name":"University of California, Santa Barbara","ror":"https://ror.org/02t274463","country_code":"US","type":"education","lineage":["https://openalex.org/I154570441"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Yuedong Wang","raw_affiliation_strings":["Department of Statistics and Applied Probability, University of California, Santa Barbara, Santa Barbara, CA;"],"affiliations":[{"raw_affiliation_string":"Department of Statistics and Applied Probability, University of California, Santa Barbara, Santa Barbara, CA;","institution_ids":["https://openalex.org/I154570441"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5036224267"],"corresponding_institution_ids":["https://openalex.org/I154570441"],"apc_list":null,"apc_paid":null,"fwci":1.7282,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.8452237,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":"31","issue":"3","first_page":"835","last_page":"845"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":0.9994999766349792,"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/T10136","display_name":"Statistical Methods and Inference","score":0.9994999766349792,"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/T11798","display_name":"Optimal Experimental Design Methods","score":0.996999979019165,"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/T10243","display_name":"Statistical Methods and Bayesian Inference","score":0.9950000047683716,"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/nonparametric-regression","display_name":"Nonparametric regression","score":0.7411150932312012},{"id":"https://openalex.org/keywords/nonparametric-statistics","display_name":"Nonparametric statistics","score":0.6596167683601379},{"id":"https://openalex.org/keywords/smoothing-spline","display_name":"Smoothing spline","score":0.6174556016921997},{"id":"https://openalex.org/keywords/covariate","display_name":"Covariate","score":0.6064408421516418},{"id":"https://openalex.org/keywords/semiparametric-regression","display_name":"Semiparametric regression","score":0.5958012342453003},{"id":"https://openalex.org/keywords/model-selection","display_name":"Model selection","score":0.5562832951545715},{"id":"https://openalex.org/keywords/variance-function","display_name":"Variance function","score":0.5554837584495544},{"id":"https://openalex.org/keywords/smoothing","display_name":"Smoothing","score":0.5437447428703308},{"id":"https://openalex.org/keywords/additive-model","display_name":"Additive model","score":0.5306462645530701},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.5268382430076599},{"id":"https://openalex.org/keywords/functional-principal-component-analysis","display_name":"Functional principal component analysis","score":0.5033974051475525},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.47231054306030273},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.47133922576904297},{"id":"https://openalex.org/keywords/functional-data-analysis","display_name":"Functional data analysis","score":0.44061028957366943},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.41656577587127686},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.41568389534950256},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.4119945168495178},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.405483216047287},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.38762447237968445},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3836566209793091},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.27099519968032837},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.26762157678604126}],"concepts":[{"id":"https://openalex.org/C74127309","wikidata":"https://www.wikidata.org/wiki/Q3455886","display_name":"Nonparametric regression","level":3,"score":0.7411150932312012},{"id":"https://openalex.org/C102366305","wikidata":"https://www.wikidata.org/wiki/Q1097688","display_name":"Nonparametric statistics","level":2,"score":0.6596167683601379},{"id":"https://openalex.org/C107457265","wikidata":"https://www.wikidata.org/wiki/Q7546460","display_name":"Smoothing spline","level":4,"score":0.6174556016921997},{"id":"https://openalex.org/C119043178","wikidata":"https://www.wikidata.org/wiki/Q320723","display_name":"Covariate","level":2,"score":0.6064408421516418},{"id":"https://openalex.org/C19539793","wikidata":"https://www.wikidata.org/wiki/Q7449609","display_name":"Semiparametric regression","level":3,"score":0.5958012342453003},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.5562832951545715},{"id":"https://openalex.org/C120488996","wikidata":"https://www.wikidata.org/wiki/Q17016064","display_name":"Variance function","level":3,"score":0.5554837584495544},{"id":"https://openalex.org/C3770464","wikidata":"https://www.wikidata.org/wiki/Q775963","display_name":"Smoothing","level":2,"score":0.5437447428703308},{"id":"https://openalex.org/C203223496","wikidata":"https://www.wikidata.org/wiki/Q4681344","display_name":"Additive model","level":2,"score":0.5306462645530701},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.5268382430076599},{"id":"https://openalex.org/C71176878","wikidata":"https://www.wikidata.org/wiki/Q17014987","display_name":"Functional principal component analysis","level":3,"score":0.5033974051475525},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.47231054306030273},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.47133922576904297},{"id":"https://openalex.org/C51820054","wikidata":"https://www.wikidata.org/wiki/Q5508814","display_name":"Functional data analysis","level":2,"score":0.44061028957366943},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.41656577587127686},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.41568389534950256},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.4119945168495178},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.405483216047287},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.38762447237968445},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3836566209793091},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.27099519968032837},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.26762157678604126},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C78458016","wikidata":"https://www.wikidata.org/wiki/Q840400","display_name":"Evolutionary biology","level":1,"score":0.0},{"id":"https://openalex.org/C205203396","wikidata":"https://www.wikidata.org/wiki/Q612143","display_name":"Bilinear interpolation","level":2,"score":0.0},{"id":"https://openalex.org/C31447003","wikidata":"https://www.wikidata.org/wiki/Q545002","display_name":"Spline interpolation","level":3,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1080/10618600.2022.2037434","is_oa":false,"landing_page_url":"https://doi.org/10.1080/10618600.2022.2037434","pdf_url":null,"source":{"id":"https://openalex.org/S76159266","display_name":"Journal of Computational and Graphical Statistics","issn_l":"1061-8600","issn":["1061-8600","1537-2715"],"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":"Journal of Computational and Graphical Statistics","raw_type":"journal-article"},{"id":"pmid:36594047","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/36594047","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America","raw_type":null},{"id":"pmh:oai:pubmedcentral.nih.gov:9802009","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/9802009","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"J Comput Graph Stat","raw_type":"Text"}],"best_oa_location":{"id":"pmh:oai:pubmedcentral.nih.gov:9802009","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/9802009","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"J Comput Graph Stat","raw_type":"Text"},"sustainable_development_goals":[{"score":0.7599999904632568,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[{"id":"https://openalex.org/G4944041158","display_name":null,"funder_award_id":"1908085MA06","funder_id":"https://openalex.org/F4320334897","funder_display_name":"Natural Science Foundation of Anhui Province"},{"id":"https://openalex.org/G5098720328","display_name":null,"funder_award_id":"11971457","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320334897","display_name":"Natural Science Foundation of Anhui Province","ror":null},{"id":"https://openalex.org/F4320337354","display_name":"National Institute of General Medical Sciences","ror":"https://ror.org/04q48ey07"},{"id":"https://openalex.org/F4320337357","display_name":"National Institute of Diabetes and Digestive and Kidney Diseases","ror":"https://ror.org/00adh9b73"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":39,"referenced_works":["https://openalex.org/W16794263","https://openalex.org/W242540098","https://openalex.org/W1487825358","https://openalex.org/W1597544196","https://openalex.org/W1603304768","https://openalex.org/W1964173605","https://openalex.org/W1993727156","https://openalex.org/W2013365346","https://openalex.org/W2015953335","https://openalex.org/W2027680244","https://openalex.org/W2038580754","https://openalex.org/W2048527384","https://openalex.org/W2056592727","https://openalex.org/W2058308334","https://openalex.org/W2059173978","https://openalex.org/W2076733016","https://openalex.org/W2082544286","https://openalex.org/W2095194401","https://openalex.org/W2144148350","https://openalex.org/W2429142782","https://openalex.org/W2742637784","https://openalex.org/W2788261908","https://openalex.org/W2797333853","https://openalex.org/W2798816298","https://openalex.org/W2831911354","https://openalex.org/W2891418335","https://openalex.org/W2902246264","https://openalex.org/W2963886019","https://openalex.org/W2987692753","https://openalex.org/W3021971632","https://openalex.org/W3037531055","https://openalex.org/W3081894066","https://openalex.org/W3082831675","https://openalex.org/W3100020851","https://openalex.org/W3101791986","https://openalex.org/W3105336892","https://openalex.org/W4246311433","https://openalex.org/W4251011993","https://openalex.org/W4298876635"],"related_works":["https://openalex.org/W2056592727","https://openalex.org/W2090567594","https://openalex.org/W2757473841","https://openalex.org/W2096061680","https://openalex.org/W2132391091","https://openalex.org/W2132911252","https://openalex.org/W4298747323","https://openalex.org/W2952664629","https://openalex.org/W2554452653","https://openalex.org/W2227516623"],"abstract_inverted_index":{"Regression":[0],"models":[1,19,81],"with":[2,59],"a":[3,39,53],"functional":[4,7,56],"response":[5],"and":[6,17,31,44,105,115,125,130,139,150,156,168],"covariate":[8],"have":[9,20],"received":[10],"significant":[11],"attention":[12],"recently.":[13],"While":[14],"various":[15],"nonparametric":[16,48,55],"semiparametric":[18],"been":[21],"developed,":[22],"there":[23],"is":[24],"an":[25],"urgent":[26],"need":[27],"for":[28,42,122,133],"model":[29,45,58,61,74,123],"selection":[30,46,124],"diagnostic":[32],"methods.":[33],"In":[34],"this":[35],"article,":[36],"we":[37],"develop":[38],"unified":[40],"framework":[41],"estimation":[43,95],"in":[47,85,142,174],"function-on-function":[49],"regression.":[50],"We":[51,92,127],"propose":[52,93],"general":[54],"regression":[57,137],"the":[60,79,86,108,111,136,147,161],"space":[62],"constructed":[63],"through":[64],"smoothing":[65],"spline":[66],"analysis":[67],"of":[68,78,110,135],"variance":[69],"(SS":[70],"ANOVA).":[71],"The":[72],"proposed":[73,162],"reduces":[75],"to":[76],"some":[77],"existing":[80],"when":[82],"selected":[83],"components":[84],"SS":[87,112],"ANOVA":[88,113],"decomposition":[89,114,144],"are":[90,172],"eliminated.":[91],"new":[94],"procedures":[96],"under":[97,145],"either":[98],"<i>L</i>":[99,102,116,148,151],"<sub>1</sub>":[100,117,149],"or":[101],"<sub>2</sub>":[103,152],"penalty":[104,118],"show":[106,159],"that":[107,160],"combination":[109],"provides":[119],"powerful":[120],"tools":[121],"diagnostics.":[126],"establish":[128],"consistency":[129],"convergence":[131],"rates":[132],"estimates":[134],"function":[138],"each":[140],"component":[141],"its":[143],"both":[146],"penalties.":[153],"Simulation":[154],"studies":[155],"real":[157],"examples":[158],"methods":[163],"perform":[164],"well.":[165],"Technical":[166],"details":[167],"additional":[169],"simulation":[170],"results":[171],"available":[173],"online":[175],"supplementary":[176],"materials.":[177]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2}],"updated_date":"2026-03-11T14:59:36.786465","created_date":"2025-10-10T00:00:00"}