{"id":"https://openalex.org/W2782560798","doi":"https://doi.org/10.1080/03610918.2017.1414248","title":"Bayesian quantile regression and variable selection for partial linear single-index model: Using free knot spline","display_name":"Bayesian quantile regression and variable selection for partial linear single-index model: Using free knot spline","publication_year":2018,"publication_date":"2018-01-16","ids":{"openalex":"https://openalex.org/W2782560798","doi":"https://doi.org/10.1080/03610918.2017.1414248","mag":"2782560798"},"language":"en","primary_location":{"id":"doi:10.1080/03610918.2017.1414248","is_oa":false,"landing_page_url":"https://doi.org/10.1080/03610918.2017.1414248","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/A5079018959","display_name":"Yu Yang","orcid":"https://orcid.org/0000-0002-2159-7382"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yang Yu","raw_affiliation_strings":["School of Economics and Management, Beihang University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Economics and Management, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101607483","display_name":"Zhihong Zou","orcid":"https://orcid.org/0000-0003-4828-3145"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhihong Zou","raw_affiliation_strings":["School of Economics and Management, Beihang University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Economics and Management, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100406258","display_name":"Shanshan Wang","orcid":"https://orcid.org/0000-0002-7205-3844"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Shanshan Wang","raw_affiliation_strings":["Beijing Key Laboratory of Emergence Support Simulation Technologies for City Operations, Beijing, China","School of Economics and Management, Beihang University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Key Laboratory of Emergence Support Simulation Technologies for City Operations, Beijing, China","institution_ids":[]},{"raw_affiliation_string":"School of Economics and Management, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100406258"],"corresponding_institution_ids":["https://openalex.org/I82880672"],"apc_list":null,"apc_paid":null,"fwci":0.2371,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.52671986,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":"48","issue":"5","first_page":"1429","last_page":"1449"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":0.9993000030517578,"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":0.9993000030517578,"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":0.9986000061035156,"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/T10243","display_name":"Statistical Methods and Bayesian Inference","score":0.9927999973297119,"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/quantile-regression","display_name":"Quantile regression","score":0.6258086562156677},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.5901484489440918},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.5765308141708374},{"id":"https://openalex.org/keywords/quantile","display_name":"Quantile","score":0.5750991702079773},{"id":"https://openalex.org/keywords/frequentist-inference","display_name":"Frequentist inference","score":0.4966161847114563},{"id":"https://openalex.org/keywords/nonparametric-regression","display_name":"Nonparametric regression","score":0.43848392367362976},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.42262959480285645},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.41985049843788147},{"id":"https://openalex.org/keywords/bayesian-multivariate-linear-regression","display_name":"Bayesian multivariate linear regression","score":0.4176537096500397},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.39171722531318665},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.38723334670066833},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.3254881203174591},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.31364452838897705},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.1740739643573761}],"concepts":[{"id":"https://openalex.org/C63817138","wikidata":"https://www.wikidata.org/wiki/Q3455889","display_name":"Quantile regression","level":2,"score":0.6258086562156677},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5901484489440918},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.5765308141708374},{"id":"https://openalex.org/C118671147","wikidata":"https://www.wikidata.org/wiki/Q578714","display_name":"Quantile","level":2,"score":0.5750991702079773},{"id":"https://openalex.org/C162376815","wikidata":"https://www.wikidata.org/wiki/Q2158281","display_name":"Frequentist inference","level":4,"score":0.4966161847114563},{"id":"https://openalex.org/C74127309","wikidata":"https://www.wikidata.org/wiki/Q3455886","display_name":"Nonparametric regression","level":3,"score":0.43848392367362976},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.42262959480285645},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.41985049843788147},{"id":"https://openalex.org/C64946054","wikidata":"https://www.wikidata.org/wiki/Q4874476","display_name":"Bayesian multivariate linear regression","level":3,"score":0.4176537096500397},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.39171722531318665},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.38723334670066833},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3254881203174591},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.31364452838897705},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.1740739643573761}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1080/03610918.2017.1414248","is_oa":false,"landing_page_url":"https://doi.org/10.1080/03610918.2017.1414248","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":[{"id":"https://openalex.org/G1845612176","display_name":null,"funder_award_id":"11501586","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5284813800","display_name":null,"funder_award_id":"71420107025","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6100735903","display_name":null,"funder_award_id":"51478025","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7299032961","display_name":null,"funder_award_id":"11701023","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"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W1969227401","https://openalex.org/W1969640518","https://openalex.org/W1978938623","https://openalex.org/W1987435863","https://openalex.org/W1989520638","https://openalex.org/W2003571586","https://openalex.org/W2020355545","https://openalex.org/W2020925091","https://openalex.org/W2025758794","https://openalex.org/W2029759029","https://openalex.org/W2030524152","https://openalex.org/W2030579408","https://openalex.org/W2032403736","https://openalex.org/W2040579578","https://openalex.org/W2043783131","https://openalex.org/W2045595693","https://openalex.org/W2048646633","https://openalex.org/W2049228615","https://openalex.org/W2054060837","https://openalex.org/W2066068511","https://openalex.org/W2070059347","https://openalex.org/W2074682976","https://openalex.org/W2080335568","https://openalex.org/W2080749331","https://openalex.org/W2080948341","https://openalex.org/W2084871407","https://openalex.org/W2086067392","https://openalex.org/W2090998835","https://openalex.org/W2091841155","https://openalex.org/W2093738981","https://openalex.org/W2106706098","https://openalex.org/W2118711140","https://openalex.org/W2128860595","https://openalex.org/W2360191141","https://openalex.org/W2524776321","https://openalex.org/W3125348112"],"related_works":["https://openalex.org/W4206511378","https://openalex.org/W4206618949","https://openalex.org/W2526321210","https://openalex.org/W3205863630","https://openalex.org/W4318833145","https://openalex.org/W2364275385","https://openalex.org/W4388704167","https://openalex.org/W2007977664","https://openalex.org/W4376874882","https://openalex.org/W2224749288"],"abstract_inverted_index":{"Partial":[0],"linear":[1,15,54],"single-index":[2],"model":[3,141],"(PLSIM)":[4],"has":[5],"both":[6],"the":[7,87,104,151],"flexibility":[8],"of":[9,14,106,117,133,142,153],"nonparametric":[10],"treatment":[11,147],"and":[12,27,49,56,72,77,123,148],"interpretability":[13],"term,":[16],"yet":[17],"existing":[18],"literatures":[19],"about":[20],"it":[21],"mainly":[22],"focused":[23],"on":[24,34],"mean":[25],"regression,":[26,48],"quantile":[28,47,78],"regression":[29,79],"analysis":[30],"is":[31,64,85],"scarce.":[32],"Based":[33],"free":[35],"knot":[36],"spline":[37],"approximation,":[38],"we":[39,136],"apply":[40],"asymmetric":[41],"Laplace":[42],"distribution":[43],"to":[44,90,109],"implement":[45,91],"Bayesian":[46,98],"perform":[50],"variable":[51,75,118],"selection":[52,76],"in":[53,69,96,114,145],"term":[55],"index":[57,120],"vector":[58,121],"via":[59],"binary":[60],"indicators.":[61],"Our":[62],"approach":[63,108],"exempt":[65],"from":[66],"regularity":[67],"conditions":[68],"frequentist":[70],"method,":[71],"could":[73],"execute":[74],"under":[80],"mutual":[81],"posterior":[82],"correction,":[83],"which":[84,112],"also":[86],"first":[88],"work":[89],"them":[92],"jointly":[93],"for":[94],"PLSIM":[95],"fully":[97],"framework.":[99],"The":[100],"numerical":[101],"simulation":[102],"manifests":[103],"superiority":[105],"our":[107],"previous":[110],"methods,":[111],"embodied":[113],"better":[115],"efficiency":[116],"selection,":[119],"estimates":[122],"link":[124],"function":[125],"approximation":[126],"with":[127],"different":[128],"error":[129],"distributions.":[130],"For":[131],"illustration":[132],"its":[134],"application,":[135],"build":[137],"a":[138],"power":[139],"consumption":[140],"A2/O":[143],"process":[144],"wastewater":[146],"emphatically":[149],"analyze":[150],"impact":[152],"water":[154],"quality":[155],"factors.":[156]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2019,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
