{"id":"https://openalex.org/W3087267488","doi":"https://doi.org/10.1002/sta4.315","title":"Expectile regression via deep residual networks","display_name":"Expectile regression via deep residual networks","publication_year":2020,"publication_date":"2020-09-18","ids":{"openalex":"https://openalex.org/W3087267488","doi":"https://doi.org/10.1002/sta4.315","mag":"3087267488"},"language":"en","primary_location":{"id":"doi:10.1002/sta4.315","doi":"https://doi.org/10.1002/sta4.315","is_oa":false,"landing_page_url":"https://doi.org/10.1002/sta4.315","pdf_url":null,"source":{"id":"https://openalex.org/S4210193439","display_name":"Stat","issn_l":"2049-1573","issn":["2049-1573"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-scientific","cwts-core","dongbi-d","jufo-1","ki-jl-1","norway-1","poland-100","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310320595","host_organization_name":"Wiley","host_organization_lineage":["https://openalex.org/P4310320595"],"host_organization_lineage_names":["Wiley"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Stat","raw_type":"journal-article"},"parent_works":[],"lineage":["https://openalex.org/W3087267488"],"child_works_count":0,"type":"article","is_traditional":true,"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/A5112566123","display_name":"Yiyi Yin","orcid":"https://orcid.org/0000-0002-7961-9214","observed_orcids":["https://orcid.org/0000-0002-7961-9214"]},"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"]},{"id":"https://openalex.org/I2800403580","display_name":"University of Minnesota System","ror":"https://ror.org/03grvy078","country_code":"US","type":"education","lineage":["https://openalex.org/I2800403580"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yiyi Yin","raw_affiliation_strings":["School of Statistics, University of Minnesota, Minneapolis, MN 55414 USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Statistics, University of Minnesota, Minneapolis, MN 55414 USA","institution_ids":["https://openalex.org/I130238516","https://openalex.org/I2800403580"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5079252402","display_name":"Hui Zou","orcid":"https://orcid.org/0000-0003-4798-9904","observed_orcids":["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"]},{"id":"https://openalex.org/I2800403580","display_name":"University of Minnesota System","ror":"https://ror.org/03grvy078","country_code":"US","type":"education","lineage":["https://openalex.org/I2800403580"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Hui Zou","raw_affiliation_strings":["School of Statistics, University of Minnesota, Minneapolis, MN 55414 USA"],"raw_orcid":"https://orcid.org/0000-0003-4798-9904","affiliations":[{"raw_affiliation_string":"School of Statistics, University of Minnesota, Minneapolis, MN 55414 USA","institution_ids":["https://openalex.org/I130238516","https://openalex.org/I2800403580"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5079252402"],"corresponding_institution_ids":["https://openalex.org/I130238516","https://openalex.org/I2800403580"],"apc_list":{"value":2700,"currency":"USD","value_usd":2700},"apc_paid":null,"fwci":0.1645,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.45705356,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":88,"max":96},"biblio":{"volume":"10","issue":"1","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":0.14589999616146088,"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":0.14589999616146088,"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/T10320","display_name":"Neural Networks and Applications","score":0.08919999748468399,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T13141","display_name":"Statistical Methods and Applications","score":0.057100001722574234,"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/expectile-regression","display_name":"expectile regression","score":0.9940000176429749},{"id":"https://openalex.org/keywords/resnet","display_name":"ResNet","score":0.8970000147819519},{"id":"https://openalex.org/keywords/nonparametric-regression","display_name":"nonparametric regression","score":0.902999997138977},{"id":"https://openalex.org/keywords/risk-measures","display_name":"risk measures","score":0.7480000257492065},{"id":"https://openalex.org/keywords/gradient-boosting","display_name":"gradient boosting","score":0.6539999842643738},{"id":"https://openalex.org/keywords/kernel-learning","display_name":"kernel learning","score":0.8040000200271606}],"concepts":[{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.6240637898445129},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.568122148513794},{"id":"https://openalex.org/C102366305","wikidata":"https://www.wikidata.org/wiki/Q1097688","display_name":"Nonparametric statistics","level":2,"score":0.5544478297233582},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.537665843963623},{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.5256558656692505},{"id":"https://openalex.org/C74127309","wikidata":"https://www.wikidata.org/wiki/Q3455886","display_name":"Nonparametric regression","level":3,"score":0.499920129776001},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.4804055988788605},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4321790039539337},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3733799159526825},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.37048226594924927},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3599132299423218},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.3247743248939514},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.09284460544586182},{"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/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1002/sta4.315","doi":"https://doi.org/10.1002/sta4.315","is_oa":false,"landing_page_url":"https://doi.org/10.1002/sta4.315","pdf_url":null,"source":{"id":"https://openalex.org/S4210193439","display_name":"Stat","issn_l":"2049-1573","issn":["2049-1573"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-scientific","cwts-core","dongbi-d","jufo-1","ki-jl-1","norway-1","poland-100","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310320595","host_organization_name":"Wiley","host_organization_lineage":["https://openalex.org/P4310320595"],"host_organization_lineage_names":["Wiley"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Stat","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[{"id":"https://openalex.org/G2131474646","display_name":null,"funder_award_id":"DMS 1915\u2010842","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G850003330","display_name":"Novel Inference Procedures for Non-Standard High-Dimensional Regression Models","funder_award_id":"2015120","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W1533861849","https://openalex.org/W1678356000","https://openalex.org/W1686810756","https://openalex.org/W1836465849","https://openalex.org/W1849277567","https://openalex.org/W1900727905","https://openalex.org/W1932847118","https://openalex.org/W1975445189","https://openalex.org/W1976486071","https://openalex.org/W2039728298","https://openalex.org/W2042590947","https://openalex.org/W2066888021","https://openalex.org/W2090347902","https://openalex.org/W2095705004","https://openalex.org/W2097117768","https://openalex.org/W2103590647","https://openalex.org/W2107877262","https://openalex.org/W2117539524","https://openalex.org/W2143612262","https://openalex.org/W2145339207","https://openalex.org/W2194775991","https://openalex.org/W2247010341","https://openalex.org/W2604272474","https://openalex.org/W2746886145","https://openalex.org/W2949117887","https://openalex.org/W2963798770","https://openalex.org/W2964121744","https://openalex.org/W2999905431","https://openalex.org/W3122170229","https://openalex.org/W3155284954","https://openalex.org/W4240805545","https://openalex.org/W4394643672"],"related_works":["https://openalex.org/W2597704325","https://openalex.org/W1975445189","https://openalex.org/W2247010341","https://openalex.org/W2128617863","https://openalex.org/W4416234996","https://openalex.org/W3016223978","https://openalex.org/W3189127602","https://openalex.org/W2001203190"],"abstract_inverted_index":{"Expectile":[0,99,111,127,141],"is":[1,19,132,143],"a":[2,54,57,82],"generalization":[3],"of":[4,53,59,126],"the":[5,17,50,90,124,144],"expected":[6],"value":[7],"in":[8,46],"probability":[9],"and":[10,14,34,37,74,96,106,137],"statistics.":[11],"In":[12,77],"finance":[13],"risk":[15,25],"management,":[16],"expectile":[18,42,65,85,151],"considered":[20],"to":[21,28,134],"be":[22,135],"an":[23],"important":[24],"measure":[26],"due":[27],"its":[29,35],"connection":[30],"with":[31,118],"gain\u2013loss":[32],"ratio":[33],"coherent":[36],"elicitable":[38],"properties.":[39],"Linear":[40],"multiple":[41],"regression":[43,66,86],"was":[44],"proposed":[45,69],"1987":[47],"for":[48,149],"estimating":[49],"conditional":[51],"expectiles":[52],"response":[55],"given":[56],"set":[58],"covariates.":[60],"Recently,":[61],"more":[62],"flexible":[63],"nonparametric":[64,84,150],"models":[67],"were":[68],"based":[70],"on":[71,104],"gradient":[72],"boosting":[73],"kernel":[75],"learning.":[76],"this":[78],"paper,":[79],"we":[80],"propose":[81],"new":[83],"model":[87,148],"by":[88,139],"adopting":[89],"deep":[91,146],"residual":[92],"network":[93],"learning":[94,147],"framework":[95],"name":[97],"it":[98,131],"NN.":[100],"Extensive":[101],"numerical":[102],"studies":[103],"simulated":[105],"real":[107],"datasets":[108],"demonstrate":[109],"that":[110,130],"NN":[112,128,142],"has":[113],"very":[114],"competitive":[115],"performance":[116],"compared":[117],"existing":[119],"methods.":[120],"We":[121],"explicitly":[122],"specify":[123],"architecture":[125],"so":[129],"easy":[133],"reproduced":[136],"used":[138],"others.":[140],"first":[145],"regression.":[152]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2012,"cited_by_count":1}],"updated_date":"2026-11-14T11:04:58.165337","created_date":"2020-09-25T00:00:00"}
