{"id":"https://openalex.org/W7160241413","doi":"https://doi.org/10.1016/j.csda.2026.108400","title":"Adaptive robust estimation for varying coefficient models","display_name":"Adaptive robust estimation for varying coefficient models","publication_year":2026,"publication_date":"2026-05-05","ids":{"openalex":"https://openalex.org/W7160241413","doi":"https://doi.org/10.1016/j.csda.2026.108400"},"language":"en","primary_location":{"id":"doi:10.1016/j.csda.2026.108400","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.csda.2026.108400","pdf_url":null,"source":{"id":"https://openalex.org/S132362803","display_name":"Computational Statistics & Data Analysis","issn_l":"0167-9473","issn":["0167-9473","1872-7352"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computational Statistics & Data Analysis","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://doi.org/10.1016/j.csda.2026.108400","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5135368075","display_name":"Tao Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I212119943","display_name":"University of Victoria","ror":"https://ror.org/04s5mat29","country_code":"CA","type":"education","lineage":["https://openalex.org/I212119943"]}],"countries":["CA"],"is_corresponding":true,"raw_author_name":"Tao Wang","raw_affiliation_strings":["Department of Economics and Department of Mathematics and Statistics (by courtesy), University of Victoria, Victoria, BC V8W 2Y2, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Economics and Department of Mathematics and Statistics (by courtesy), University of Victoria, Victoria, BC V8W 2Y2, Canada","institution_ids":["https://openalex.org/I212119943"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5025749190","display_name":"Weixin Yao","orcid":"https://orcid.org/0000-0001-5925-5081"},"institutions":[{"id":"https://openalex.org/I103635307","display_name":"University of California, Riverside","ror":"https://ror.org/03nawhv43","country_code":"US","type":"education","lineage":["https://openalex.org/I103635307"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Weixin Yao","raw_affiliation_strings":["Department of Statistics, University of California, Riverside, CA 92521, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics, University of California, Riverside, CA 92521, USA","institution_ids":["https://openalex.org/I103635307"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5135368075"],"corresponding_institution_ids":["https://openalex.org/I212119943"],"apc_list":{"value":3340,"currency":"USD","value_usd":3340},"apc_paid":{"value":3340,"currency":"USD","value_usd":3340},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.91501817,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":null,"biblio":{"volume":"222","issue":null,"first_page":"108400","last_page":"108400"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":0.7168999910354614,"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":0.7168999910354614,"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":0.06300000101327896,"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/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.022600000724196434,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.6891000270843506},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.676800012588501},{"id":"https://openalex.org/keywords/smoothing","display_name":"Smoothing","score":0.6037999987602234},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.460999995470047},{"id":"https://openalex.org/keywords/estimation-theory","display_name":"Estimation theory","score":0.45829999446868896},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.4260999858379364},{"id":"https://openalex.org/keywords/adaptive-estimator","display_name":"Adaptive estimator","score":0.4027000069618225},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.38679999113082886}],"concepts":[{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.6891000270843506},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.676800012588501},{"id":"https://openalex.org/C3770464","wikidata":"https://www.wikidata.org/wiki/Q775963","display_name":"Smoothing","level":2,"score":0.6037999987602234},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5220000147819519},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.5153999924659729},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.460999995470047},{"id":"https://openalex.org/C167928553","wikidata":"https://www.wikidata.org/wiki/Q1376021","display_name":"Estimation theory","level":2,"score":0.45829999446868896},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4487000107765198},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.4260999858379364},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.40639999508857727},{"id":"https://openalex.org/C2780617739","wikidata":"https://www.wikidata.org/wiki/Q4680738","display_name":"Adaptive estimator","level":3,"score":0.4027000069618225},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.38679999113082886},{"id":"https://openalex.org/C67226441","wikidata":"https://www.wikidata.org/wiki/Q1665389","display_name":"Robust statistics","level":3,"score":0.3864000141620636},{"id":"https://openalex.org/C151376022","wikidata":"https://www.wikidata.org/wiki/Q168698","display_name":"Exponential function","level":2,"score":0.38029998540878296},{"id":"https://openalex.org/C102634674","wikidata":"https://www.wikidata.org/wiki/Q868473","display_name":"Smoothness","level":2,"score":0.3732999861240387},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.37040001153945923},{"id":"https://openalex.org/C9936470","wikidata":"https://www.wikidata.org/wiki/Q6510405","display_name":"Least-squares function approximation","level":3,"score":0.34610000252723694},{"id":"https://openalex.org/C133710760","wikidata":"https://www.wikidata.org/wiki/Q775837","display_name":"Exponential smoothing","level":2,"score":0.34209999442100525},{"id":"https://openalex.org/C159694833","wikidata":"https://www.wikidata.org/wiki/Q2321565","display_name":"Iterative method","level":2,"score":0.32739999890327454},{"id":"https://openalex.org/C137668524","wikidata":"https://www.wikidata.org/wiki/Q189813","display_name":"Censoring (clinical trials)","level":2,"score":0.31679999828338623},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.30250000953674316},{"id":"https://openalex.org/C70259352","wikidata":"https://www.wikidata.org/wiki/Q1847839","display_name":"Robust regression","level":3,"score":0.27950000762939453},{"id":"https://openalex.org/C126090379","wikidata":"https://www.wikidata.org/wiki/Q6094424","display_name":"Iteratively reweighted least squares","level":4,"score":0.26989999413490295},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.267300009727478},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2590000033378601},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.2556000053882599}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1016/j.csda.2026.108400","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.csda.2026.108400","pdf_url":null,"source":{"id":"https://openalex.org/S132362803","display_name":"Computational Statistics & Data Analysis","issn_l":"0167-9473","issn":["0167-9473","1872-7352"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computational Statistics & Data Analysis","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1016/j.csda.2026.108400","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.csda.2026.108400","pdf_url":null,"source":{"id":"https://openalex.org/S132362803","display_name":"Computational Statistics & Data Analysis","issn_l":"0167-9473","issn":["0167-9473","1872-7352"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computational Statistics & Data Analysis","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320334593","display_name":"Natural Sciences and Engineering Research Council of Canada","ror":"https://ror.org/01h531d29"},{"id":"https://openalex.org/F4320334617","display_name":"Social Sciences and Humanities Research Council of Canada","ror":"https://ror.org/04j5jqy92"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":41,"referenced_works":["https://openalex.org/W1143914011","https://openalex.org/W1835490675","https://openalex.org/W1971278385","https://openalex.org/W2000303778","https://openalex.org/W2020226329","https://openalex.org/W2027655461","https://openalex.org/W2029469881","https://openalex.org/W2029992122","https://openalex.org/W2033079433","https://openalex.org/W2043429637","https://openalex.org/W2050284024","https://openalex.org/W2060467039","https://openalex.org/W2064850188","https://openalex.org/W2067653997","https://openalex.org/W2069627663","https://openalex.org/W2086680598","https://openalex.org/W2091615269","https://openalex.org/W2092367563","https://openalex.org/W2094607565","https://openalex.org/W2145411167","https://openalex.org/W2165001683","https://openalex.org/W2167031636","https://openalex.org/W2235327544","https://openalex.org/W2282275465","https://openalex.org/W2469479275","https://openalex.org/W2503808305","https://openalex.org/W2509991828","https://openalex.org/W2951493603","https://openalex.org/W3089507321","https://openalex.org/W3152816661","https://openalex.org/W3202523284","https://openalex.org/W4308826760","https://openalex.org/W4322747663","https://openalex.org/W4367845396","https://openalex.org/W4379881278","https://openalex.org/W4381893290","https://openalex.org/W4387397720","https://openalex.org/W4395046903","https://openalex.org/W4398767357","https://openalex.org/W4402636043","https://openalex.org/W4408952239"],"related_works":[],"abstract_inverted_index":{"An":[0],"adaptive":[1,42,116],"robust":[2,68],"estimation":[3,56,155],"framework":[4],"is":[5,96],"developed":[6],"for":[7,98],"varying":[8],"coefficient":[9,126],"models":[10],"using":[11],"the":[12,73,100,105,140],"exponential":[13,106],"squared":[14,107],"loss,":[15],"offering":[16],"enhanced":[17],"resistance":[18],"to":[19,109,121],"extreme":[20],"observations":[21],"and":[22,49,89,92,135,144,157],"heavy-tailed":[23],"error":[24],"distributions.":[25],"The":[26,115],"methodology":[27],"departs":[28],"from":[29],"traditional":[30],"approaches":[31],"that":[32,70],"rely":[33],"on":[34,139],"a":[35,67,93,145],"uniform":[36],"smoothing":[37,48],"parameter":[38,102],"by":[39],"employing":[40],"coefficient-specific":[41],"bandwidths,":[43],"thereby":[44],"enabling":[45],"locally":[46],"optimal":[47],"improving":[50],"statistical":[51],"efficiency.":[52],"A":[53],"local":[54],"kernel-weighted":[55],"scheme":[57],"combined":[58],"with":[59,104,161],"an":[60],"iterative":[61],"reweighted":[62],"least":[63],"squares":[64],"algorithm":[65],"produces":[66],"estimator":[69],"effectively":[71],"limits":[72],"influence":[74],"of":[75],"outliers":[76],"while":[77],"preserving":[78],"high":[79],"efficiency":[80],"under":[81],"standard":[82],"regularity":[83],"conditions.":[84],"Theoretical":[85],"results":[86],"establish":[87],"consistency":[88],"asymptotic":[90],"normality,":[91],"data-driven":[94],"procedure":[95],"introduced":[97],"selecting":[99],"tuning":[101],"associated":[103],"loss":[108],"ensure":[110],"efficient":[111],"performance":[112,159],"in":[113,153],"practice.":[114],"bandwidth":[117],"mechanism":[118],"further":[119],"adjusts":[120],"heterogeneous":[122],"smoothness":[123],"patterns":[124],"across":[125],"functions,":[127],"delivering":[128],"favorable":[129],"bias-variance":[130],"trade-offs.":[131],"Monte":[132],"Carlo":[133],"studies":[134],"empirical":[136],"analyses":[137],"based":[138],"Boston":[141],"Housing":[142],"data":[143],"real-world":[146],"bike-sharing":[147],"usage":[148],"dataset":[149],"demonstrate":[150],"substantial":[151],"gains":[152],"robustness,":[154],"accuracy,":[156],"predictive":[158],"compared":[160],"existing":[162],"competing":[163],"methods.":[164]},"counts_by_year":[],"updated_date":"2026-05-10T06:08:35.923825","created_date":"2026-05-06T00:00:00"}