{"id":"https://openalex.org/W4387670247","doi":"https://doi.org/10.1080/03610918.2023.2265592","title":"Robust statistical inference for varying-coefficient partially linear instrumental variable model based on modal regression","display_name":"Robust statistical inference for varying-coefficient partially linear instrumental variable model based on modal regression","publication_year":2023,"publication_date":"2023-10-15","ids":{"openalex":"https://openalex.org/W4387670247","doi":"https://doi.org/10.1080/03610918.2023.2265592"},"language":"en","primary_location":{"id":"doi:10.1080/03610918.2023.2265592","is_oa":false,"landing_page_url":"https://doi.org/10.1080/03610918.2023.2265592","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/A5016174358","display_name":"Yanting Xiao","orcid":"https://orcid.org/0000-0002-4694-2648"},"institutions":[{"id":"https://openalex.org/I4210131919","display_name":"Xi'an University of Technology","ror":"https://ror.org/038avdt50","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210131919"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yanting Xiao","raw_affiliation_strings":["Department of Applied Mathematics, Xi\u2019an University of Technology","Department of Applied Mathematics, Xi\u2019an University of Technology, Xi\u2019an, P. R. China"],"affiliations":[{"raw_affiliation_string":"Department of Applied Mathematics, Xi\u2019an University of Technology","institution_ids":["https://openalex.org/I4210131919"]},{"raw_affiliation_string":"Department of Applied Mathematics, Xi\u2019an University of Technology, Xi\u2019an, P. R. China","institution_ids":["https://openalex.org/I4210131919"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5113023821","display_name":"Wanying Dong","orcid":null},"institutions":[{"id":"https://openalex.org/I4210131919","display_name":"Xi'an University of Technology","ror":"https://ror.org/038avdt50","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210131919"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wanying Dong","raw_affiliation_strings":["Department of Applied Mathematics, Xi\u2019an University of Technology","Department of Applied Mathematics, Xi\u2019an University of Technology, Xi\u2019an, P. R. China"],"affiliations":[{"raw_affiliation_string":"Department of Applied Mathematics, Xi\u2019an University of Technology","institution_ids":["https://openalex.org/I4210131919"]},{"raw_affiliation_string":"Department of Applied Mathematics, Xi\u2019an University of Technology, Xi\u2019an, P. R. China","institution_ids":["https://openalex.org/I4210131919"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5016174358"],"corresponding_institution_ids":["https://openalex.org/I4210131919"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.1492474,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"54","issue":"3","first_page":"925","last_page":"941"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11871","display_name":"Advanced Statistical Methods and Models","score":0.9986000061035156,"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/T11871","display_name":"Advanced Statistical Methods and Models","score":0.9986000061035156,"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/T10136","display_name":"Statistical Methods and Inference","score":0.9983000159263611,"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":0.9975000023841858,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/instrumental-variable","display_name":"Instrumental variable","score":0.6861335039138794},{"id":"https://openalex.org/keywords/nonparametric-statistics","display_name":"Nonparametric statistics","score":0.6716009378433228},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.6682760715484619},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.6591665148735046},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.6366361975669861},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.5693284869194031},{"id":"https://openalex.org/keywords/parametric-statistics","display_name":"Parametric statistics","score":0.525201141834259},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.494851291179657},{"id":"https://openalex.org/keywords/covariate","display_name":"Covariate","score":0.48856815695762634},{"id":"https://openalex.org/keywords/linear-regression","display_name":"Linear regression","score":0.4796160161495209},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.32159191370010376},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.17113611102104187}],"concepts":[{"id":"https://openalex.org/C162144332","wikidata":"https://www.wikidata.org/wiki/Q1665305","display_name":"Instrumental variable","level":2,"score":0.6861335039138794},{"id":"https://openalex.org/C102366305","wikidata":"https://www.wikidata.org/wiki/Q1097688","display_name":"Nonparametric statistics","level":2,"score":0.6716009378433228},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.6682760715484619},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.6591665148735046},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.6366361975669861},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5693284869194031},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.525201141834259},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.494851291179657},{"id":"https://openalex.org/C119043178","wikidata":"https://www.wikidata.org/wiki/Q320723","display_name":"Covariate","level":2,"score":0.48856815695762634},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.4796160161495209},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.32159191370010376},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.17113611102104187}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1080/03610918.2023.2265592","is_oa":false,"landing_page_url":"https://doi.org/10.1080/03610918.2023.2265592","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":25,"referenced_works":["https://openalex.org/W1560144238","https://openalex.org/W1935597517","https://openalex.org/W2024828559","https://openalex.org/W2066906300","https://openalex.org/W2074682976","https://openalex.org/W2088870374","https://openalex.org/W2090309891","https://openalex.org/W2090998835","https://openalex.org/W2146050890","https://openalex.org/W2151355802","https://openalex.org/W2161457086","https://openalex.org/W2172523399","https://openalex.org/W2342208003","https://openalex.org/W2401649869","https://openalex.org/W2407823423","https://openalex.org/W2588070562","https://openalex.org/W2752368500","https://openalex.org/W2791148629","https://openalex.org/W2793342610","https://openalex.org/W2914483702","https://openalex.org/W3014469272","https://openalex.org/W3020389026","https://openalex.org/W3021467908","https://openalex.org/W3140498024","https://openalex.org/W4230104359"],"related_works":["https://openalex.org/W2051418878","https://openalex.org/W2985746494","https://openalex.org/W4206042385","https://openalex.org/W2511384863","https://openalex.org/W2080773131","https://openalex.org/W2096089271","https://openalex.org/W2923628599","https://openalex.org/W2014100433","https://openalex.org/W2170152280","https://openalex.org/W3122148829"],"abstract_inverted_index":{"AbstractWe":[0],"study":[1],"the":[2,40,51,54,70,77,83,90,104,133,140],"variable":[3,16,28],"selection":[4,29,68,85],"for":[5,31,89],"varying-coefficient":[6],"partially":[7,118],"linear":[8,119],"model":[9],"with":[10],"some":[11],"endogenous":[12,55],"covariates.":[13],"Combining":[14],"instrumental":[15],"adjustment":[17],"technology":[18],"and":[19,26,35,42,57,86,98,151],"modal":[20],"regression,":[21],"we":[22],"develop":[23],"an":[24],"efficient":[25],"robust":[27,59],"procedure":[30,48,92,108],"selecting":[32],"significant":[33],"parametric":[34],"nonparametric":[36,43],"components":[37],"simultaneously,":[38],"estimating":[39],"parameter":[41],"function":[44],"consistently.":[45],"The":[46],"proposed":[47,91],"can":[49],"attenuate":[50],"effect":[52],"of":[53,69,76,106,128,144,147],"variables,":[56],"is":[58,112,137],"against":[60],"outliers":[61],"or":[62],"heavy-tail":[63],"error":[64],"distributions.":[65],"With":[66],"appropriate":[67],"tuning":[71],"parameters,":[72],"certain":[73],"asymptotic":[74],"properties":[75],"resulting":[78],"estimators":[79],"are":[80,93],"established.":[81],"Moreover,":[82],"bandwidth":[84],"estimation":[87],"algorithm":[88],"discussed.":[94],"Some":[95],"simulation":[96],"results":[97],"a":[99],"real":[100],"example":[101],"confirm":[102],"that":[103],"performance":[105],"our":[107],"in":[109],"finite":[110],"samples":[111],"satisfactory.Keywords:":[113],"Instrumental":[114],"variableModal":[115],"regressionVariable":[116],"selectionVarying-coefficient":[117],"modelMATHEMATICS":[120],"SUBJECT":[121],"CLASSIFICATION:":[122],"62G1062G05":[123],"Disclosure":[124],"statementNo":[125],"potential":[126],"conflict":[127],"interest":[129],"was":[130],"reported":[131],"by":[132,139],"authors.Additional":[134],"informationFundingThis":[135],"work":[136],"supported":[138],"Natural":[141],"Science":[142],"Foundation":[143],"Shaanxi":[145],"Province":[146],"China":[148],"(No.":[149],"2022JM-027":[150],"2021JQ-485).":[152]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
