{"id":"https://openalex.org/W2562941480","doi":"https://doi.org/10.48550/arxiv.1612.03974","title":"A self-calibrating method for heavy tailed data modelling. Application in neuroscience and finance","display_name":"A self-calibrating method for heavy tailed data modelling. Application in neuroscience and finance","publication_year":2016,"publication_date":"2016-12-12","ids":{"openalex":"https://openalex.org/W2562941480","doi":"https://doi.org/10.48550/arxiv.1612.03974","mag":"2562941480"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:1612.03974","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/1612.03974","pdf_url":"https://arxiv.org/pdf/1612.03974","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"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":"text"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1612.03974","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5020676999","display_name":"Nehla Debbabi","orcid":"https://orcid.org/0000-0003-0299-3002","observed_orcids":["https://orcid.org/0000-0003-0299-3002"]},"institutions":[{"id":"https://openalex.org/I4210100943","display_name":"Centre de Recherche en Sciences et Technologies de l'Information et de la Communication","ror":"https://ror.org/016wdna72","country_code":"FR","type":"facility","lineage":["https://openalex.org/I154526488","https://openalex.org/I4210100943","https://openalex.org/I4210141980","https://openalex.org/I96226040"]},{"id":"https://openalex.org/I4210112335","display_name":"\u00c9cole Sup\u00e9rieure Priv\u00e9e d'Ing\u00e9nierie et de Technologies","ror":"https://ror.org/021f99908","country_code":"TN","type":"education","lineage":["https://openalex.org/I4210112335"]},{"id":"https://openalex.org/I96226040","display_name":"Universit\u00e9 de Reims Champagne-Ardenne","ror":"https://ror.org/03hypw319","country_code":"FR","type":"education","lineage":["https://openalex.org/I96226040"]}],"countries":["FR","TN"],"is_corresponding":false,"raw_author_name":"Debbabi, Nehla","raw_affiliation_strings":["ESPRIT School of Engineering , Tunis , Tunisia,","Universit\u00e9 de Reims Champagne Ardenne , CReSTIC Lab. , France ,"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"ESPRIT School of Engineering , Tunis , Tunisia,","institution_ids":["https://openalex.org/I4210112335"]},{"raw_affiliation_string":"Universit\u00e9 de Reims Champagne Ardenne , CReSTIC Lab. , France ,","institution_ids":["https://openalex.org/I4210100943","https://openalex.org/I96226040"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080205972","display_name":"Marie F. Kratz","orcid":"https://orcid.org/0000-0001-5160-2042","observed_orcids":["https://orcid.org/0000-0001-5160-2042"]},"institutions":[{"id":"https://openalex.org/I124261006","display_name":"\u00c9cole Sup\u00e9rieure des Sciences \u00c9conomiques et Commerciales","ror":"https://ror.org/02dga6j42","country_code":"FR","type":"other","lineage":["https://openalex.org/I124261006"]},{"id":"https://openalex.org/I4405277924","display_name":"ESSEC Business School","ror":"https://ror.org/04be2h179","country_code":"SG","type":"education","lineage":["https://openalex.org/I4405277924"]}],"countries":["FR","SG"],"is_corresponding":false,"raw_author_name":"Kratz, Marie","raw_affiliation_strings":["ESSEC Business School , CREAR risk research center"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"ESSEC Business School , CREAR risk research center","institution_ids":["https://openalex.org/I124261006","https://openalex.org/I4405277924"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5006802147","display_name":"Mamadou Mboup","orcid":"https://orcid.org/0000-0002-9212-7038","observed_orcids":["https://orcid.org/0000-0002-9212-7038"]},"institutions":[{"id":"https://openalex.org/I4210100943","display_name":"Centre de Recherche en Sciences et Technologies de l'Information et de la Communication","ror":"https://ror.org/016wdna72","country_code":"FR","type":"facility","lineage":["https://openalex.org/I154526488","https://openalex.org/I4210100943","https://openalex.org/I4210141980","https://openalex.org/I96226040"]},{"id":"https://openalex.org/I96226040","display_name":"Universit\u00e9 de Reims Champagne-Ardenne","ror":"https://ror.org/03hypw319","country_code":"FR","type":"education","lineage":["https://openalex.org/I96226040"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Mboup, Mamadou","raw_affiliation_strings":["Universit\u00e9 de Reims Champagne Ardenne , CReSTIC Lab. , France ,"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Universit\u00e9 de Reims Champagne Ardenne , CReSTIC Lab. , France ,","institution_ids":["https://openalex.org/I4210100943","https://openalex.org/I96226040"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":7,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.36010000109672546,"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"}},"topics":[{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.36010000109672546,"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/T10968","display_name":"Statistical Distribution Estimation and Applications","score":0.3441999852657318,"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/T11720","display_name":"Probability and Risk Models","score":0.25270000100135803,"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/heavy-tailed-distributions","display_name":"heavy-tailed distributions","score":0.8119999766349792},{"id":"https://openalex.org/keywords/self-calibration","display_name":"self-calibration","score":0.7450000047683716},{"id":"https://openalex.org/keywords/hybrid-distribution","display_name":"hybrid distribution","score":0.4569999873638153},{"id":"https://openalex.org/keywords/generalized-pareto-distribution","display_name":"generalized Pareto distribution","score":0.8980000019073486},{"id":"https://openalex.org/keywords/exponential-distribution","display_name":"exponential distribution","score":0.49300000071525574},{"id":"https://openalex.org/keywords/unsupervised-parameter-estimation","display_name":"unsupervised parameter estimation","score":0.8930000066757202},{"id":"https://openalex.org/keywords/convergence-analysis","display_name":"convergence analysis","score":0.7839999794960022},{"id":"https://openalex.org/keywords/extreme-value-theory","display_name":"extreme value theory","score":0.9380000233650208},{"id":"https://openalex.org/keywords/neuroscience-data","display_name":"neuroscience data","score":0.8510000109672546},{"id":"https://openalex.org/keywords/financial-data","display_name":"financial data","score":0.9319999814033508}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4470948576927185},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.43431416153907776},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.41420820355415344},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.24458616971969604}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:1612.03974","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/1612.03974","pdf_url":"https://arxiv.org/pdf/1612.03974","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"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":"text"},{"id":"doi:10.48550/arxiv.1612.03974","doi":"https://doi.org/10.48550/arxiv.1612.03974","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1612.03974","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1612.03974","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/1612.03974","pdf_url":"https://arxiv.org/pdf/1612.03974","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"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":"text"},"sustainable_development_goals":[],"study_designs":[],"awards":[{"id":"https://openalex.org/G4108850584","display_name":"\"Risk Analysis, Ruin and  Extremes\"","funder_award_id":"318984","funder_id":"https://openalex.org/F4320320300","funder_display_name":"European Commission"}],"funders":[{"id":"https://openalex.org/F4320320300","display_name":"European Commission","ror":"https://ror.org/00k4n6c32"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2562941480.pdf","grobid_xml":"https://content.openalex.org/works/W2562941480.grobid-xml"},"referenced_works_count":28,"referenced_works":["https://openalex.org/W128533561","https://openalex.org/W1498464051","https://openalex.org/W1539158587","https://openalex.org/W1540484164","https://openalex.org/W1810054000","https://openalex.org/W1852465236","https://openalex.org/W1964449911","https://openalex.org/W1966738572","https://openalex.org/W1980620433","https://openalex.org/W2000822760","https://openalex.org/W2011416738","https://openalex.org/W2036983007","https://openalex.org/W2037819340","https://openalex.org/W2048649579","https://openalex.org/W2087070363","https://openalex.org/W2091656757","https://openalex.org/W2118020555","https://openalex.org/W2135669648","https://openalex.org/W2136686018","https://openalex.org/W2160458348","https://openalex.org/W2165333293","https://openalex.org/W2170021787","https://openalex.org/W2264883459","https://openalex.org/W2290960099","https://openalex.org/W2312194337","https://openalex.org/W2520584115","https://openalex.org/W2547788783","https://openalex.org/W2613001510"],"related_works":["https://openalex.org/W3103317372","https://openalex.org/W4408224467","https://openalex.org/W7118076709","https://openalex.org/W2476389657","https://openalex.org/W7168269069","https://openalex.org/W7128545436","https://openalex.org/W4402129082","https://openalex.org/W1563267031","https://openalex.org/W4298165797","https://openalex.org/W4416048524"],"abstract_inverted_index":{"Modelling":[0],"non-homogeneous":[1],"and":[2,25,97,116,139,154],"multi-component":[3,65],"data":[4,66,136],"is":[5,18,35,80,103,126],"a":[6,23,60,73,77,83],"problem":[7],"that":[8,67,91],"challenges":[9],"scientific":[10],"researchers":[11],"in":[12],"several":[13],"fields.":[14],"In":[15,55],"general,":[16],"it":[17,133],"not":[19],"possible":[20],"to":[21,30,40,63,82,134],"find":[22],"simple":[24],"closed":[26],"form":[27],"probabilistic":[28],"model":[29,64],"describe":[31],"such":[32],"data.":[33],"That":[34],"why":[36],"one":[37,86],"often":[38],"resorts":[39],"non-parametric":[41],"approaches.":[42],"However,":[43],"when":[44],"the":[45,93,108,123,152,155],"multiple":[46],"components":[47],"are":[48],"separable,":[49],"parametric":[50],"modelling":[51],"becomes":[52],"again":[53],"tractable.":[54],"this":[56,111,159],"study,":[57],"we":[58],"propose":[59],"self-calibrating":[61,124],"method":[62,125],"exhibit":[68],"heavy":[69],"tails.":[70],"We":[71,113],"introduce":[72],"three-component":[74],"hybrid":[75],"distribution:":[76],"Gaussian":[78],"distribution":[79,90],"linked":[81],"Generalized":[84],"Pareto":[85],"via":[87],"an":[88],"exponential":[89],"bridges":[92],"gap":[94],"between":[95],"mean":[96],"tail":[98],"behaviors.":[99],"An":[100],"unsupervised":[101],"algorithm":[102],"then":[104],"developed":[105],"for":[106],"estimating":[107],"parameters":[109],"of":[110,122,158],"model.":[112],"study":[114],"analytically":[115],"numerically":[117],"its":[118],"convergence.":[119],"The":[120],"effectiveness":[121],"tested":[127],"on":[128],"simulated":[129],"data,":[130],"before":[131],"applying":[132],"real":[135],"from":[137],"neuroscience":[138],"finance,":[140],"respectively.":[141],"A":[142],"comparison":[143],"with":[144],"other":[145],"standard":[146],"Extreme":[147],"Value":[148],"Theory":[149],"approaches":[150],"confirms":[151],"relevance":[153],"practical":[156],"advantage":[157],"new":[160],"method.":[161]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2017-01-06T00:00:00"}
