{"id":"https://openalex.org/W2786324233","doi":"https://doi.org/10.1007/s10260-018-0421-7","title":"A note on variable selection in functional regression via random subspace method","display_name":"A note on variable selection in functional regression via random subspace method","publication_year":2018,"publication_date":"2018-01-25","ids":{"openalex":"https://openalex.org/W2786324233","doi":"https://doi.org/10.1007/s10260-018-0421-7","mag":"2786324233"},"language":"en","primary_location":{"id":"doi:10.1007/s10260-018-0421-7","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10260-018-0421-7","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10260-018-0421-7.pdf","source":{"id":"https://openalex.org/S155615518","display_name":"Statistical Methods & Applications","issn_l":"1121-9130","issn":["1121-9130","1613-981X","1618-2510","2385-264X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Statistical Methods &amp; Applications","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://link.springer.com/content/pdf/10.1007/s10260-018-0421-7.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5050473776","display_name":"\u0141ukasz Smaga","orcid":"https://orcid.org/0000-0002-2442-8816"},"institutions":[{"id":"https://openalex.org/I59411706","display_name":"Adam Mickiewicz University in Pozna\u0144","ror":"https://ror.org/04g6bbq64","country_code":"PL","type":"education","lineage":["https://openalex.org/I59411706"]}],"countries":["PL"],"is_corresponding":true,"raw_author_name":"\u0141ukasz Smaga","raw_affiliation_strings":["Faculty of Mathematics and Computer Science, Adam Mickiewicz University, Umultowska 87, 61-614, Pozna\u0144, Poland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Mathematics and Computer Science, Adam Mickiewicz University, Umultowska 87, 61-614, Pozna\u0144, Poland","institution_ids":["https://openalex.org/I59411706"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5019815418","display_name":"Hidetoshi Matsui","orcid":"https://orcid.org/0000-0002-6286-5072"},"institutions":[{"id":"https://openalex.org/I171494771","display_name":"Shiga University","ror":"https://ror.org/01vvhy971","country_code":"JP","type":"education","lineage":["https://openalex.org/I171494771"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Hidetoshi Matsui","raw_affiliation_strings":["Faculty of Data Science, Shiga University, 1-1-1, Banba, Hikone, Shiga, 522-8522, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Data Science, Shiga University, 1-1-1, Banba, Hikone, Shiga, 522-8522, Japan","institution_ids":["https://openalex.org/I171494771"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5050473776"],"corresponding_institution_ids":["https://openalex.org/I59411706"],"apc_list":{"value":2990,"currency":"USD","value_usd":2990},"apc_paid":{"value":2990,"currency":"USD","value_usd":2990},"fwci":0.6311,"has_fulltext":true,"cited_by_count":7,"citation_normalized_percentile":{"value":0.67988572,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"27","issue":"3","first_page":"455","last_page":"477"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":0.9991000294685364,"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":0.9991000294685364,"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":0.9976999759674072,"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":0.9948999881744385,"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/feature-selection","display_name":"Feature selection","score":0.5817370414733887},{"id":"https://openalex.org/keywords/model-selection","display_name":"Model selection","score":0.5555703639984131},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.5516484975814819},{"id":"https://openalex.org/keywords/scalar","display_name":"Scalar (mathematics)","score":0.5488107204437256},{"id":"https://openalex.org/keywords/proper-linear-model","display_name":"Proper linear model","score":0.5409674644470215},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.5212653279304504},{"id":"https://openalex.org/keywords/linear-regression","display_name":"Linear regression","score":0.4853347837924957},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.42769360542297363},{"id":"https://openalex.org/keywords/subspace-topology","display_name":"Subspace topology","score":0.4258262515068054},{"id":"https://openalex.org/keywords/random-variable","display_name":"Random variable","score":0.4195348620414734},{"id":"https://openalex.org/keywords/linear-model","display_name":"Linear model","score":0.4167029857635498},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.41365665197372437},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.3543028235435486},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.3096647262573242},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.26530104875564575},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2622157335281372},{"id":"https://openalex.org/keywords/bayesian-multivariate-linear-regression","display_name":"Bayesian multivariate linear regression","score":0.17474383115768433}],"concepts":[{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.5817370414733887},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.5555703639984131},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.5516484975814819},{"id":"https://openalex.org/C57691317","wikidata":"https://www.wikidata.org/wiki/Q1289248","display_name":"Scalar (mathematics)","level":2,"score":0.5488107204437256},{"id":"https://openalex.org/C32224588","wikidata":"https://www.wikidata.org/wiki/Q7250175","display_name":"Proper linear model","level":4,"score":0.5409674644470215},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5212653279304504},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.4853347837924957},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.42769360542297363},{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.4258262515068054},{"id":"https://openalex.org/C122123141","wikidata":"https://www.wikidata.org/wiki/Q176623","display_name":"Random variable","level":2,"score":0.4195348620414734},{"id":"https://openalex.org/C163175372","wikidata":"https://www.wikidata.org/wiki/Q3339222","display_name":"Linear model","level":2,"score":0.4167029857635498},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.41365665197372437},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.3543028235435486},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3096647262573242},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.26530104875564575},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2622157335281372},{"id":"https://openalex.org/C64946054","wikidata":"https://www.wikidata.org/wiki/Q4874476","display_name":"Bayesian multivariate linear regression","level":3,"score":0.17474383115768433},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1007/s10260-018-0421-7","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10260-018-0421-7","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10260-018-0421-7.pdf","source":{"id":"https://openalex.org/S155615518","display_name":"Statistical Methods & Applications","issn_l":"1121-9130","issn":["1121-9130","1613-981X","1618-2510","2385-264X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Statistical Methods &amp; Applications","raw_type":"journal-article"},{"id":"pmh:oai:RePEc:spr:stmapp:v:27:y:2018:i:3:d:10.1007_s10260-018-0421-7","is_oa":false,"landing_page_url":"http://link.springer.com/10.1007/s10260-018-0421-7","pdf_url":null,"source":{"id":"https://openalex.org/S4306401271","display_name":"RePEc: Research Papers in Economics","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I77793887","host_organization_name":"Federal Reserve Bank of St. Louis","host_organization_lineage":["https://openalex.org/I77793887"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.1007/s10260-018-0421-7","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10260-018-0421-7","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10260-018-0421-7.pdf","source":{"id":"https://openalex.org/S155615518","display_name":"Statistical Methods & Applications","issn_l":"1121-9130","issn":["1121-9130","1613-981X","1618-2510","2385-264X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Statistical Methods &amp; Applications","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3130131490","display_name":"Higher order improvement of statistical inference based on the unification of several nonparametric methods","funder_award_id":"16H02790","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"},{"id":"https://openalex.org/G7567498047","display_name":"Formulation of statistical models for longitudinal data and estimation by the sparse regularization","funder_award_id":"16K16020","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"}],"funders":[{"id":"https://openalex.org/F4320334764","display_name":"Japan Society for the Promotion of Science","ror":"https://ror.org/00hhkn466"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2786324233.pdf","grobid_xml":"https://content.openalex.org/works/W2786324233.grobid-xml"},"referenced_works_count":56,"referenced_works":["https://openalex.org/W611353362","https://openalex.org/W619520426","https://openalex.org/W621923488","https://openalex.org/W1565746575","https://openalex.org/W1576898078","https://openalex.org/W1583788335","https://openalex.org/W1589400987","https://openalex.org/W1596515083","https://openalex.org/W1603304768","https://openalex.org/W1604822381","https://openalex.org/W1981426103","https://openalex.org/W1984827805","https://openalex.org/W1995775640","https://openalex.org/W2000950277","https://openalex.org/W2001952407","https://openalex.org/W2008493423","https://openalex.org/W2029758616","https://openalex.org/W2033779771","https://openalex.org/W2043753405","https://openalex.org/W2047054206","https://openalex.org/W2047114443","https://openalex.org/W2048159935","https://openalex.org/W2048694650","https://openalex.org/W2055810607","https://openalex.org/W2059173978","https://openalex.org/W2067165060","https://openalex.org/W2069802481","https://openalex.org/W2069880366","https://openalex.org/W2070971872","https://openalex.org/W2072250033","https://openalex.org/W2072308660","https://openalex.org/W2075174355","https://openalex.org/W2076258896","https://openalex.org/W2086294300","https://openalex.org/W2126063633","https://openalex.org/W2180438109","https://openalex.org/W2181591901","https://openalex.org/W2289747895","https://openalex.org/W2325803261","https://openalex.org/W2470652322","https://openalex.org/W2559200333","https://openalex.org/W2582743722","https://openalex.org/W2609278581","https://openalex.org/W2886919222","https://openalex.org/W2897001073","https://openalex.org/W3021971632","https://openalex.org/W3099619512","https://openalex.org/W3100020851","https://openalex.org/W3100980645","https://openalex.org/W3101275405","https://openalex.org/W3101449523","https://openalex.org/W3137136951","https://openalex.org/W3151142116","https://openalex.org/W4205462863","https://openalex.org/W4241897277","https://openalex.org/W4292156489"],"related_works":["https://openalex.org/W3093365844","https://openalex.org/W4311364185","https://openalex.org/W4309298396","https://openalex.org/W4220829648","https://openalex.org/W2383348664","https://openalex.org/W2750360204","https://openalex.org/W2240243152","https://openalex.org/W2356254167","https://openalex.org/W2289575421","https://openalex.org/W1572348857"],"abstract_inverted_index":{"Abstract":[0],"Variable":[1],"selection":[2,85,123],"problem":[3,25],"is":[4,36,99],"one":[5],"of":[6,29,61,72,120,142],"the":[7,27,138],"most":[8],"important":[9],"tasks":[10],"in":[11,15,26,137,140],"regression":[12,33,56,91],"analysis,":[13],"especially":[14],"a":[16,37,112],"high-dimensional":[17],"setting.":[18],"In":[19],"this":[20,24,66],"paper,":[21],"we":[22],"study":[23],"context":[28],"scalar":[30,41,88],"response":[31,42,89],"functional":[32,44,47,62,90,97],"model,":[34,145],"which":[35],"linear":[38,55],"model":[39,48,57,67,92,98],"with":[40],"and":[43,68,74,111,150],"regressors.":[45],"The":[46,95],"can":[49],"be":[50],"represented":[51],"by":[52,101],"certain":[53],"multiple":[54],"via":[58],"basis":[59],"expansions":[60],"variables.":[63],"Based":[64],"on":[65],"random":[69],"subspace":[70],"method":[71],"Mielniczuk":[73],"Teisseyre":[75],"(Comput":[76],"Stat":[77],"Data":[78],"Anal":[79],"71:725\u2013742,":[80],"2014),":[81],"two":[82],"simple":[83],"variable":[84,122],"procedures":[86,133],"for":[87],"are":[93],"proposed.":[94],"final":[96],"selected":[100,144],"using":[102],"generalized":[103],"information":[104],"criteria.":[105],"Monte":[106],"Carlo":[107],"simulation":[108],"studies":[109],"conducted":[110],"real":[113],"data":[114],"example":[115],"show":[116],"very":[117],"satisfactory":[118],"performance":[119],"new":[121],"methods":[124],"under":[125],"finite":[126],"samples.":[127],"Moreover,":[128],"they":[129],"suggest":[130],"that":[131],"considered":[132],"outperform":[134],"solutions":[135],"found":[136],"literature":[139],"terms":[141],"correctly":[143],"false":[146],"discovery":[147],"rate":[148],"control":[149],"prediction":[151],"error.":[152]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1},{"year":2019,"cited_by_count":2}],"updated_date":"2026-07-31T08:31:51.225901","created_date":"2025-10-10T00:00:00"}
