{"id":"https://openalex.org/W2247010341","doi":"https://doi.org/10.1080/00401706.2017.1291450","title":"Flexible Expectile Regression in Reproducing Kernel Hilbert Spaces","display_name":"Flexible Expectile Regression in Reproducing Kernel Hilbert Spaces","publication_year":2017,"publication_date":"2017-02-08","ids":{"openalex":"https://openalex.org/W2247010341","doi":"https://doi.org/10.1080/00401706.2017.1291450","mag":"2247010341"},"language":"en","primary_location":{"id":"doi:10.1080/00401706.2017.1291450","is_oa":false,"landing_page_url":"https://doi.org/10.1080/00401706.2017.1291450","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","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://stars.library.ucf.edu/scopus2015/9253","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5062321525","display_name":"Yi Yang","orcid":"https://orcid.org/0000-0002-1471-4026"},"institutions":[{"id":"https://openalex.org/I5023651","display_name":"McGill University","ror":"https://ror.org/01pxwe438","country_code":"CA","type":"education","lineage":["https://openalex.org/I5023651"]}],"countries":["CA"],"is_corresponding":true,"raw_author_name":"Yi Yang","raw_affiliation_strings":["Department of Mathematics and Statistics, McGill University, Montr\u00e9al, QC, Canada"],"affiliations":[{"raw_affiliation_string":"Department of Mathematics and Statistics, McGill University, Montr\u00e9al, QC, Canada","institution_ids":["https://openalex.org/I5023651"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101852303","display_name":"Teng Zhang","orcid":"https://orcid.org/0000-0001-8378-1073"},"institutions":[{"id":"https://openalex.org/I106165777","display_name":"University of Central Florida","ror":"https://ror.org/036nfer12","country_code":"US","type":"education","lineage":["https://openalex.org/I106165777"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Teng Zhang","raw_affiliation_strings":["Department of Mathematics, University of Central Florida, Orlando, FL"],"affiliations":[{"raw_affiliation_string":"Department of Mathematics, University of Central Florida, Orlando, FL","institution_ids":["https://openalex.org/I106165777"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5079252402","display_name":"Hui Zou","orcid":"https://orcid.org/0000-0003-4798-9904"},"institutions":[{"id":"https://openalex.org/I130238516","display_name":"University of Minnesota","ror":"https://ror.org/017zqws13","country_code":"US","type":"education","lineage":["https://openalex.org/I130238516"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hui Zou","raw_affiliation_strings":["School of Statistics, University of Minnesota, Minneapolis, MN"],"affiliations":[{"raw_affiliation_string":"School of Statistics, University of Minnesota, Minneapolis, MN","institution_ids":["https://openalex.org/I130238516"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5062321525"],"corresponding_institution_ids":["https://openalex.org/I5023651"],"apc_list":null,"apc_paid":null,"fwci":2.1577,"has_fulltext":false,"cited_by_count":21,"citation_normalized_percentile":{"value":0.89855918,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"60","issue":"1","first_page":"26","last_page":"35"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":0.9995999932289124,"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":0.9995999932289124,"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/T11236","display_name":"Control Systems and Identification","score":0.9943000078201294,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11871","display_name":"Advanced Statistical Methods and Models","score":0.9933000206947327,"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/estimator","display_name":"Estimator","score":0.6310244798660278},{"id":"https://openalex.org/keywords/kernel-regression","display_name":"Kernel regression","score":0.5959934592247009},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.5598496794700623},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.5237148404121399},{"id":"https://openalex.org/keywords/kernel-method","display_name":"Kernel method","score":0.4853009581565857},{"id":"https://openalex.org/keywords/reproducing-kernel-hilbert-space","display_name":"Reproducing kernel Hilbert space","score":0.4644681513309479},{"id":"https://openalex.org/keywords/nonparametric-regression","display_name":"Nonparametric regression","score":0.4430280327796936},{"id":"https://openalex.org/keywords/hilbert-space","display_name":"Hilbert space","score":0.4269367754459381},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.42588597536087036},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.41444098949432373},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.3832562267780304},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.36906033754348755},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.3265148997306824},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.2850196957588196},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.25842106342315674},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.15095114707946777},{"id":"https://openalex.org/keywords/discrete-mathematics","display_name":"Discrete mathematics","score":0.11656144261360168}],"concepts":[{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.6310244798660278},{"id":"https://openalex.org/C200695384","wikidata":"https://www.wikidata.org/wiki/Q1739319","display_name":"Kernel regression","level":3,"score":0.5959934592247009},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5598496794700623},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.5237148404121399},{"id":"https://openalex.org/C122280245","wikidata":"https://www.wikidata.org/wiki/Q620622","display_name":"Kernel method","level":3,"score":0.4853009581565857},{"id":"https://openalex.org/C80884492","wikidata":"https://www.wikidata.org/wiki/Q3345678","display_name":"Reproducing kernel Hilbert space","level":3,"score":0.4644681513309479},{"id":"https://openalex.org/C74127309","wikidata":"https://www.wikidata.org/wiki/Q3455886","display_name":"Nonparametric regression","level":3,"score":0.4430280327796936},{"id":"https://openalex.org/C62799726","wikidata":"https://www.wikidata.org/wiki/Q190056","display_name":"Hilbert space","level":2,"score":0.4269367754459381},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.42588597536087036},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.41444098949432373},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3832562267780304},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.36906033754348755},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.3265148997306824},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2850196957588196},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.25842106342315674},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.15095114707946777},{"id":"https://openalex.org/C118615104","wikidata":"https://www.wikidata.org/wiki/Q121416","display_name":"Discrete mathematics","level":1,"score":0.11656144261360168},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1080/00401706.2017.1291450","is_oa":false,"landing_page_url":"https://doi.org/10.1080/00401706.2017.1291450","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:stars.library.ucf.edu:scopus2015-10252","is_oa":true,"landing_page_url":"https://stars.library.ucf.edu/scopus2015/9253","pdf_url":null,"source":{"id":"https://openalex.org/S4210172555","display_name":"Journal of International Crisis and Risk Communication Research","issn_l":"2576-0017","issn":["2576-0017","2576-0025"],"is_oa":true,"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Scopus Export 2015-2019","raw_type":"text"},{"id":"pmh:oai:figshare.com:article/5117911","is_oa":true,"landing_page_url":"https://figshare.com/articles/journal_contribution/Flexible_Expectile_Regression_in_Reproducing_Kernel_Hilbert_Spaces/5117911","pdf_url":null,"source":{"id":"https://openalex.org/S4377196282","display_name":"Figshare","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210132348","host_organization_name":"Figshare (United Kingdom)","host_organization_lineage":["https://openalex.org/I4210132348"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Text"},{"id":"doi:10.6084/m9.figshare.5117911.v1","is_oa":true,"landing_page_url":"https://doi.org/10.6084/m9.figshare.5117911.v1","pdf_url":null,"source":{"id":"https://openalex.org/S4377196282","display_name":"Figshare","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210132348","host_organization_name":"Figshare (United Kingdom)","host_organization_lineage":["https://openalex.org/I4210132348"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article-journal"}],"best_oa_location":{"id":"pmh:oai:stars.library.ucf.edu:scopus2015-10252","is_oa":true,"landing_page_url":"https://stars.library.ucf.edu/scopus2015/9253","pdf_url":null,"source":{"id":"https://openalex.org/S4210172555","display_name":"Journal of International Crisis and Risk Communication Research","issn_l":"2576-0017","issn":["2576-0017","2576-0025"],"is_oa":true,"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Scopus Export 2015-2019","raw_type":"text"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G6182544424","display_name":null,"funder_award_id":"DMS-1505111","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":38,"referenced_works":["https://openalex.org/W1539150806","https://openalex.org/W1554944419","https://openalex.org/W1562874436","https://openalex.org/W1678356000","https://openalex.org/W1975445189","https://openalex.org/W1983576086","https://openalex.org/W1986333823","https://openalex.org/W2007154098","https://openalex.org/W2013182691","https://openalex.org/W2019291268","https://openalex.org/W2023227312","https://openalex.org/W2039284087","https://openalex.org/W2039728298","https://openalex.org/W2042590947","https://openalex.org/W2043800867","https://openalex.org/W2044421224","https://openalex.org/W2061260390","https://openalex.org/W2064735708","https://openalex.org/W2075965721","https://openalex.org/W2078394884","https://openalex.org/W2101950184","https://openalex.org/W2103590647","https://openalex.org/W2105292945","https://openalex.org/W2107877262","https://openalex.org/W2109588405","https://openalex.org/W2128054485","https://openalex.org/W2146766088","https://openalex.org/W2149414429","https://openalex.org/W2408196097","https://openalex.org/W2787894218","https://openalex.org/W2798750840","https://openalex.org/W3102781765","https://openalex.org/W3105330742","https://openalex.org/W3123498107","https://openalex.org/W3123960162","https://openalex.org/W3125329023","https://openalex.org/W4298876635","https://openalex.org/W4300029251"],"related_works":["https://openalex.org/W2038278625","https://openalex.org/W2964184113","https://openalex.org/W4298112926","https://openalex.org/W2018264451","https://openalex.org/W2037269069","https://openalex.org/W4241084170","https://openalex.org/W4280628650","https://openalex.org/W2125264841","https://openalex.org/W2164358887","https://openalex.org/W2951912223"],"abstract_inverted_index":{"Expectile,":[0],"first":[1],"introduced":[2],"by":[3,76,147,172,217],"Newey":[4,77],"and":[5,12,21,45,59,78,85,94,151],"Powell":[6,79],"in":[7,33,42,80,124,154],"1987":[8,81],"Newey,":[9,82],"W.":[10,83],"K.,":[11,84],"Powell,":[13,86],"J.":[14,87],"L.":[15,88],"(1987),":[16,89],"\u201cAsymmetric":[17,90],"Least":[18,91],"Squares":[19,92],"Estimation":[20,93],"Testing,\u201d":[22,95],"Econometrica,":[23,96],"55,":[24,97],"819\u2013847.[Crossref],":[25,98],"[Web":[26,99],"of":[27,100,109,162,181,208,215],"Science":[28,101],"\u00ae]":[29,102],",":[30,103],"[Google":[31,104],"Scholar]":[32],"the":[34,70,141,155,177,187,202,213],"econometrics":[35],"literature,":[36],"has":[37,137],"recently":[38],"become":[39],"increasingly":[40],"popular":[41],"risk":[43],"management":[44],"capital":[46],"allocation":[47],"for":[48,65,175,225],"financial":[49],"institutions":[50],"due":[51],"to":[52,114,200],"its":[53],"desirable":[54],"properties":[55],"such":[56],"as":[57],"coherence":[58],"elicitability.":[60],"The":[61,106,130,158],"current":[62],"standard":[63],"tool":[64],"expectile":[66,73,110,122,145],"regression":[67,74,111,123,146],"analysis":[68],"is":[69,133,164,184],"multiple":[71,121,138,143],"linear":[72,144,194],"proposed":[75],"Scholar].":[105],"growing":[107],"applications":[108],"motivate":[112],"us":[113],"develop":[115,167],"a":[116,125,193],"much":[117],"more":[118],"flexible":[119],"nonparametric":[120],"reproducing":[126],"kernel":[127,159],"Hilbert":[128],"space.":[129],"resulting":[131],"estimator":[132],"called":[134],"KERE,":[135],"which":[136],"advantages":[139],"over":[140],"classical":[142],"incorporating":[148],"nonlinearity,":[149],"nonadditivity,":[150],"complex":[152],"interactions":[153],"final":[156],"estimator.":[157],"learning":[160],"theory":[161],"KERE":[163,216],"established.":[165],"We":[166,210],"an":[168],"efficient":[169],"algorithm":[170,188],"inspired":[171],"majorization-minimization":[173],"principle":[174],"solving":[176],"entire":[178],"solution":[179],"path":[180],"KERE.":[182,209],"It":[183],"shown":[185],"that":[186],"converges":[189],"at":[190,192],"least":[191],"rate.":[195],"Extensive":[196],"simulations":[197],"are":[198,228],"conducted":[199],"show":[201],"very":[203],"competitive":[204],"finite":[205],"sample":[206],"performance":[207],"further":[211],"demonstrate":[212],"application":[214],"using":[218],"personal":[219],"computer":[220],"price":[221],"data.":[222],"Supplementary":[223],"materials":[224],"this":[226],"article":[227],"available":[229],"online.":[230]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":6},{"year":2020,"cited_by_count":5},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":2},{"year":2017,"cited_by_count":1}],"updated_date":"2026-03-11T14:59:36.786465","created_date":"2025-10-10T00:00:00"}
