{"id":"https://openalex.org/W4319791352","doi":"https://doi.org/10.1007/s00180-023-01330-y","title":"Bootstrapping binary GEV regressions for imbalanced datasets","display_name":"Bootstrapping binary GEV regressions for imbalanced datasets","publication_year":2023,"publication_date":"2023-02-04","ids":{"openalex":"https://openalex.org/W4319791352","doi":"https://doi.org/10.1007/s00180-023-01330-y"},"language":"en","primary_location":{"id":"doi:10.1007/s00180-023-01330-y","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s00180-023-01330-y","pdf_url":"https://link.springer.com/content/pdf/10.1007/s00180-023-01330-y.pdf","source":{"id":"https://openalex.org/S8500805","display_name":"Computational Statistics","issn_l":"0943-4062","issn":["0943-4062","1613-9658"],"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":"Computational Statistics","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/s00180-023-01330-y.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5014127212","display_name":"Michele La Rocca","orcid":"https://orcid.org/0000-0002-8768-4606"},"institutions":[{"id":"https://openalex.org/I131729948","display_name":"University of Salerno","ror":"https://ror.org/0192m2k53","country_code":"IT","type":"education","lineage":["https://openalex.org/I131729948"]}],"countries":["IT"],"is_corresponding":true,"raw_author_name":"Michele La Rocca","raw_affiliation_strings":["Department of Economics and Statistics, University of Salerno, Via Giovanni Paolo II, 132, 84084, Fisciano, Salerno, Italy"],"affiliations":[{"raw_affiliation_string":"Department of Economics and Statistics, University of Salerno, Via Giovanni Paolo II, 132, 84084, Fisciano, Salerno, Italy","institution_ids":["https://openalex.org/I131729948"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070343891","display_name":"Marcella Niglio","orcid":"https://orcid.org/0000-0002-4220-4277"},"institutions":[{"id":"https://openalex.org/I131729948","display_name":"University of Salerno","ror":"https://ror.org/0192m2k53","country_code":"IT","type":"education","lineage":["https://openalex.org/I131729948"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Marcella Niglio","raw_affiliation_strings":["Department of Economics and Statistics, University of Salerno, Via Giovanni Paolo II, 132, 84084, Fisciano, Salerno, Italy"],"affiliations":[{"raw_affiliation_string":"Department of Economics and Statistics, University of Salerno, Via Giovanni Paolo II, 132, 84084, Fisciano, Salerno, Italy","institution_ids":["https://openalex.org/I131729948"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5048753915","display_name":"Marialuisa Restaino","orcid":"https://orcid.org/0000-0002-1150-8278"},"institutions":[{"id":"https://openalex.org/I131729948","display_name":"University of Salerno","ror":"https://ror.org/0192m2k53","country_code":"IT","type":"education","lineage":["https://openalex.org/I131729948"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Marialuisa Restaino","raw_affiliation_strings":["Department of Economics and Statistics, University of Salerno, Via Giovanni Paolo II, 132, 84084, Fisciano, Salerno, Italy"],"affiliations":[{"raw_affiliation_string":"Department of Economics and Statistics, University of Salerno, Via Giovanni Paolo II, 132, 84084, Fisciano, Salerno, Italy","institution_ids":["https://openalex.org/I131729948"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5014127212"],"corresponding_institution_ids":["https://openalex.org/I131729948"],"apc_list":{"value":2490,"currency":"EUR","value_usd":3090},"apc_paid":{"value":2490,"currency":"EUR","value_usd":3090},"fwci":1.3933,"has_fulltext":true,"cited_by_count":4,"citation_normalized_percentile":{"value":0.80569248,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":"39","issue":"1","first_page":"181","last_page":"213"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10968","display_name":"Statistical Distribution Estimation and Applications","score":0.9977999925613403,"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/T10968","display_name":"Statistical Distribution Estimation and Applications","score":0.9977999925613403,"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":0.9976000189781189,"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":0.9948999881744385,"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/bootstrapping","display_name":"Bootstrapping (finance)","score":0.6216176152229309},{"id":"https://openalex.org/keywords/resampling","display_name":"Resampling","score":0.6115618348121643},{"id":"https://openalex.org/keywords/variable","display_name":"Variable (mathematics)","score":0.47591549158096313},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.47126173973083496},{"id":"https://openalex.org/keywords/quantile","display_name":"Quantile","score":0.4689934551715851},{"id":"https://openalex.org/keywords/binary-classification","display_name":"Binary classification","score":0.435888409614563},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.4170264005661011},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4152894616127014},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.4104993939399719},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3241817355155945},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.32018929719924927},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.1686686873435974},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.11758854985237122}],"concepts":[{"id":"https://openalex.org/C207609745","wikidata":"https://www.wikidata.org/wiki/Q4944086","display_name":"Bootstrapping (finance)","level":2,"score":0.6216176152229309},{"id":"https://openalex.org/C150921843","wikidata":"https://www.wikidata.org/wiki/Q1170431","display_name":"Resampling","level":2,"score":0.6115618348121643},{"id":"https://openalex.org/C182365436","wikidata":"https://www.wikidata.org/wiki/Q50701","display_name":"Variable (mathematics)","level":2,"score":0.47591549158096313},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.47126173973083496},{"id":"https://openalex.org/C118671147","wikidata":"https://www.wikidata.org/wiki/Q578714","display_name":"Quantile","level":2,"score":0.4689934551715851},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.435888409614563},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.4170264005661011},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4152894616127014},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.4104993939399719},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3241817355155945},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.32018929719924927},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.1686686873435974},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.11758854985237122},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"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/C78458016","wikidata":"https://www.wikidata.org/wiki/Q840400","display_name":"Evolutionary biology","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1007/s00180-023-01330-y","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s00180-023-01330-y","pdf_url":"https://link.springer.com/content/pdf/10.1007/s00180-023-01330-y.pdf","source":{"id":"https://openalex.org/S8500805","display_name":"Computational Statistics","issn_l":"0943-4062","issn":["0943-4062","1613-9658"],"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":"Computational Statistics","raw_type":"journal-article"},{"id":"pmh:oai:RePEc:spr:compst:v:39:y:2024:i:1:d:10.1007_s00180-023-01330-y","is_oa":false,"landing_page_url":"http://link.springer.com/10.1007/s00180-023-01330-y","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/s00180-023-01330-y","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s00180-023-01330-y","pdf_url":"https://link.springer.com/content/pdf/10.1007/s00180-023-01330-y.pdf","source":{"id":"https://openalex.org/S8500805","display_name":"Computational Statistics","issn_l":"0943-4062","issn":["0943-4062","1613-9658"],"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":"Computational Statistics","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320324133","display_name":"Universit\u00e0 degli Studi di Salerno","ror":"https://ror.org/0192m2k53"}],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4319791352.pdf"},"referenced_works_count":34,"referenced_works":["https://openalex.org/W1500657154","https://openalex.org/W1507985183","https://openalex.org/W1909800943","https://openalex.org/W1941659294","https://openalex.org/W1967298524","https://openalex.org/W1982663673","https://openalex.org/W1995200249","https://openalex.org/W2015452969","https://openalex.org/W2021869006","https://openalex.org/W2023639956","https://openalex.org/W2052218905","https://openalex.org/W2068301711","https://openalex.org/W2076983043","https://openalex.org/W2080735729","https://openalex.org/W2124181495","https://openalex.org/W2139120543","https://openalex.org/W2148143831","https://openalex.org/W2154063878","https://openalex.org/W2164187839","https://openalex.org/W2165910495","https://openalex.org/W2287300176","https://openalex.org/W2327088997","https://openalex.org/W2338318698","https://openalex.org/W2562319768","https://openalex.org/W2584211670","https://openalex.org/W2980714219","https://openalex.org/W3007995834","https://openalex.org/W3100094457","https://openalex.org/W3124838210","https://openalex.org/W3125458234","https://openalex.org/W3125937743","https://openalex.org/W4205598956","https://openalex.org/W4300858224","https://openalex.org/W4301861531"],"related_works":["https://openalex.org/W1488761988","https://openalex.org/W1534274833","https://openalex.org/W2044551864","https://openalex.org/W1965635691","https://openalex.org/W2375771286","https://openalex.org/W69222743","https://openalex.org/W2030885862","https://openalex.org/W1913467779","https://openalex.org/W4229900761","https://openalex.org/W2149345204"],"abstract_inverted_index":{"Abstract":[0],"This":[1],"paper":[2],"proposes":[3],"and":[4,29,85,160,201],"discusses":[5],"a":[6,21,63,87,186,210],"bootstrap":[7,80,103,120,124,149,178],"scheme":[8,151],"to":[9,62,81,91,111,139,209,213],"make":[10],"inferences":[11],"when":[12,129],"an":[13,46,218],"imbalance":[14,36,194],"in":[15,37,118,170,179,197,217],"one":[16],"of":[17,20,31,54,65,95,101,132,175,205],"the":[18,26,32,35,38,52,55,77,93,112,123,130,133,145,148,156,165,176,193,198,206],"levels":[19],"binary":[22,39],"variable":[23,28,41,200],"affects":[24],"both":[25],"dependent":[27,40,199],"some":[30],"features.":[33,97,202],"Specifically,":[34],"is":[42,183,195,221],"managed":[43],"by":[44],"adopting":[45],"asymmetric":[47],"link":[48,134,157,167],"function":[49,135,158,168],"based":[50],"on":[51],"quantile":[53],"generalized":[56],"extreme":[57],"value":[58],"(GEV)":[59],"distribution,":[60],"leading":[61],"class":[64,114],"models":[66],"called":[67],"GEV":[68,166,180],"regression":[69,181],".":[70],"Within":[71],"this":[72,171],"framework,":[73],"we":[74],"propose":[75],"using":[76,185],"fractional-random-weighted":[78],"(FRW)":[79],"obtain":[82],"confidence":[83],"intervals":[84],"implement":[86],"multiple":[88],"testing":[89],"procedure":[90],"identifying":[92],"set":[94],"relevant":[96],"The":[98,173],"main":[99],"advantages":[100],"FRW":[102,177],"are":[104,115],"as":[105],"follows:":[106],"(1)":[107],"all":[108],"observations":[109],"belonging":[110],"imbalanced":[113],"always":[116],"present":[117,196],"every":[119],"resample;":[121],"(2)":[122],"can":[125,161],"be":[126,162],"applied":[127,163],"even":[128],"complexity":[131],"does":[136,152],"not":[137,153],"allow":[138],"easily":[140],"compute":[141],"second-order":[142],"derivatives":[143],"for":[144],"Hessian;":[146],"(3)":[147],"resampling":[150],"change":[154],"whatever":[155],"is,":[159],"beyond":[164],"used":[169],"study.":[172],"performance":[174],"modelling":[182],"evaluated":[184],"detailed":[187],"Monte":[188],"Carlo":[189],"simulation":[190],"study,":[191],"where":[192],"An":[203],"application":[204],"proposed":[207],"methodology":[208],"real":[211],"dataset":[212],"analyze":[214],"student":[215],"churn":[216],"Italian":[219],"university":[220],"also":[222],"discussed.":[223]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":2}],"updated_date":"2025-12-22T23:10:17.713674","created_date":"2025-10-10T00:00:00"}
