{"id":"https://openalex.org/W2913160357","doi":"https://doi.org/10.1080/03610918.2018.1563152","title":"Nonparametric dependence modeling via cluster analysis: A financial contagion application","display_name":"Nonparametric dependence modeling via cluster analysis: A financial contagion application","publication_year":2019,"publication_date":"2019-02-10","ids":{"openalex":"https://openalex.org/W2913160357","doi":"https://doi.org/10.1080/03610918.2018.1563152","mag":"2913160357"},"language":"en","primary_location":{"id":"doi:10.1080/03610918.2018.1563152","is_oa":false,"landing_page_url":"https://doi.org/10.1080/03610918.2018.1563152","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/A5033727615","display_name":"Ricardo Fonseca Couto","orcid":null},"institutions":[{"id":"https://openalex.org/I90844405","display_name":"Instituto Brasileiro de Mercado de Capitais","ror":"https://ror.org/0101h5125","country_code":"BR","type":"education","lineage":["https://openalex.org/I90844405"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Ricardo Couto","raw_affiliation_strings":["Department of Economics, IBMEC Business School, Rio de Janeiro, Brazil;"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Economics, IBMEC Business School, Rio de Janeiro, Brazil;","institution_ids":["https://openalex.org/I90844405"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008318292","display_name":"Luiz H. Duczmal","orcid":"https://orcid.org/0000-0002-0577-4107"},"institutions":[{"id":"https://openalex.org/I110200422","display_name":"Universidade Federal de Minas Gerais","ror":"https://ror.org/0176yjw32","country_code":"BR","type":"education","lineage":["https://openalex.org/I110200422"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Luiz H. Duczmal","raw_affiliation_strings":["Department of Statistics, Federal University of Minas, Gerais, Brazil;"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics, Federal University of Minas, Gerais, Brazil;","institution_ids":["https://openalex.org/I110200422"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001699571","display_name":"Denise Burgarelli","orcid":null},"institutions":[{"id":"https://openalex.org/I110200422","display_name":"Universidade Federal de Minas Gerais","ror":"https://ror.org/0176yjw32","country_code":"BR","type":"education","lineage":["https://openalex.org/I110200422"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Denise Burgarelli","raw_affiliation_strings":["Department of Mathematics, Federal University of Minas, Gerais, Brazil;"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Mathematics, Federal University of Minas, Gerais, Brazil;","institution_ids":["https://openalex.org/I110200422"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054894722","display_name":"Felipe \u00c1lvares","orcid":null},"institutions":[{"id":"https://openalex.org/I110200422","display_name":"Universidade Federal de Minas Gerais","ror":"https://ror.org/0176yjw32","country_code":"BR","type":"education","lineage":["https://openalex.org/I110200422"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Felipe \u00c1lvares","raw_affiliation_strings":["Department of Statistics, Federal University of Minas, Gerais, Brazil;"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics, Federal University of Minas, Gerais, Brazil;","institution_ids":["https://openalex.org/I110200422"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5015111666","display_name":"Gladston Moreira","orcid":"https://orcid.org/0000-0001-7747-5926"},"institutions":[{"id":"https://openalex.org/I10824318","display_name":"Universidade Federal de Ouro Preto","ror":"https://ror.org/056s65p46","country_code":"BR","type":"education","lineage":["https://openalex.org/I10824318"]}],"countries":["BR"],"is_corresponding":true,"raw_author_name":"Gladston J. P. Moreira","raw_affiliation_strings":["Computing Department, Universidade Federal de Ouro Preto, Ouro Preto, Brazil"],"raw_orcid":"https://orcid.org/0000-0001-7747-5926","affiliations":[{"raw_affiliation_string":"Computing Department, Universidade Federal de Ouro Preto, Ouro Preto, Brazil","institution_ids":["https://openalex.org/I10824318"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5015111666"],"corresponding_institution_ids":["https://openalex.org/I10824318"],"apc_list":null,"apc_paid":null,"fwci":0.5486,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.79911355,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":95},"biblio":{"volume":"50","issue":"2","first_page":"537","last_page":"556"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11270","display_name":"Complex Systems and Time Series Analysis","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11270","display_name":"Complex Systems and Time Series Analysis","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10282","display_name":"Financial Risk and Volatility Modeling","score":0.9988999962806702,"subfield":{"id":"https://openalex.org/subfields/2003","display_name":"Finance"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11059","display_name":"Market Dynamics and Volatility","score":0.994700014591217,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/copula","display_name":"Copula (linguistics)","score":0.7010347247123718},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.6125496029853821},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5346402525901794},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5250358581542969},{"id":"https://openalex.org/keywords/nonparametric-statistics","display_name":"Nonparametric statistics","score":0.5055787563323975},{"id":"https://openalex.org/keywords/weighting","display_name":"Weighting","score":0.47634461522102356},{"id":"https://openalex.org/keywords/bivariate-analysis","display_name":"Bivariate analysis","score":0.4613198935985565},{"id":"https://openalex.org/keywords/tail-dependence","display_name":"Tail dependence","score":0.4237847328186035},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3199996054172516},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.23237735033035278},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.20986729860305786}],"concepts":[{"id":"https://openalex.org/C17618745","wikidata":"https://www.wikidata.org/wiki/Q207509","display_name":"Copula (linguistics)","level":2,"score":0.7010347247123718},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.6125496029853821},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5346402525901794},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5250358581542969},{"id":"https://openalex.org/C102366305","wikidata":"https://www.wikidata.org/wiki/Q1097688","display_name":"Nonparametric statistics","level":2,"score":0.5055787563323975},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.47634461522102356},{"id":"https://openalex.org/C64341305","wikidata":"https://www.wikidata.org/wiki/Q4919225","display_name":"Bivariate analysis","level":2,"score":0.4613198935985565},{"id":"https://openalex.org/C2777606061","wikidata":"https://www.wikidata.org/wiki/Q16991115","display_name":"Tail dependence","level":3,"score":0.4237847328186035},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3199996054172516},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.23237735033035278},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.20986729860305786},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.0},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1080/03610918.2018.1563152","is_oa":false,"landing_page_url":"https://doi.org/10.1080/03610918.2018.1563152","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":[{"id":"https://metadata.un.org/sdg/2","display_name":"Zero hunger","score":0.550000011920929}],"awards":[{"id":"https://openalex.org/G8356576669","display_name":null,"funder_award_id":"311710/2016-6","funder_id":"https://openalex.org/F4320322025","funder_display_name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico"}],"funders":[{"id":"https://openalex.org/F4320322025","display_name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico","ror":"https://ror.org/03swz6y49"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":68,"referenced_works":["https://openalex.org/W188522900","https://openalex.org/W207625288","https://openalex.org/W1596804121","https://openalex.org/W1601581105","https://openalex.org/W1727659491","https://openalex.org/W1809668037","https://openalex.org/W1964166103","https://openalex.org/W1968756693","https://openalex.org/W1969418588","https://openalex.org/W1975558989","https://openalex.org/W1977536776","https://openalex.org/W1978608550","https://openalex.org/W1980651976","https://openalex.org/W1983874169","https://openalex.org/W1984505857","https://openalex.org/W1991370590","https://openalex.org/W1992819771","https://openalex.org/W1999454708","https://openalex.org/W2002151188","https://openalex.org/W2004588151","https://openalex.org/W2005432509","https://openalex.org/W2008693707","https://openalex.org/W2009492905","https://openalex.org/W2030462521","https://openalex.org/W2040012172","https://openalex.org/W2053857213","https://openalex.org/W2067426246","https://openalex.org/W2069936503","https://openalex.org/W2078981043","https://openalex.org/W2079661833","https://openalex.org/W2087284513","https://openalex.org/W2092924973","https://openalex.org/W2119608553","https://openalex.org/W2123729673","https://openalex.org/W2126135255","https://openalex.org/W2128532428","https://openalex.org/W2130166596","https://openalex.org/W2139130494","https://openalex.org/W2139400178","https://openalex.org/W2139710299","https://openalex.org/W2143512982","https://openalex.org/W2149908462","https://openalex.org/W2163540932","https://openalex.org/W2165565175","https://openalex.org/W2165714389","https://openalex.org/W2167666676","https://openalex.org/W2169439765","https://openalex.org/W2337790911","https://openalex.org/W2490213682","https://openalex.org/W2501921488","https://openalex.org/W2528407048","https://openalex.org/W2558239789","https://openalex.org/W3121371856","https://openalex.org/W3121453935","https://openalex.org/W3121630491","https://openalex.org/W3122171427","https://openalex.org/W3122566258","https://openalex.org/W3125007389","https://openalex.org/W3125564657","https://openalex.org/W3148551660","https://openalex.org/W4211253574","https://openalex.org/W4229982665","https://openalex.org/W4239339957","https://openalex.org/W4245758359","https://openalex.org/W4247545505","https://openalex.org/W4248503640","https://openalex.org/W4298197486","https://openalex.org/W4301082726"],"related_works":["https://openalex.org/W2990837948","https://openalex.org/W3127236696","https://openalex.org/W2202466617","https://openalex.org/W2756923888","https://openalex.org/W2080551413","https://openalex.org/W2024311735","https://openalex.org/W3126452883","https://openalex.org/W2025420088","https://openalex.org/W4287838929","https://openalex.org/W1964879778"],"abstract_inverted_index":{"Dependence":[0],"measures,":[1,32],"from":[2,49],"linear":[3],"correlation":[4],"coefficients":[5],"to":[6,14,85,135,151,178],"recent":[7],"copula-based":[8],"methods,":[9],"have":[10],"been":[11],"widely":[12],"used":[13,150],"find":[15],"out":[16],"associations":[17,89],"between":[18,90],"variables.":[19],"Although":[20],"the":[21,99,118,137,142,153,157,164,174,184],"latter":[22],"type":[23],"of":[24,30,117,141,156],"measure":[25],"has":[26,34],"overcome":[27],"many":[28],"drawbacks":[29],"traditional":[31],"copula":[33,189],"intrinsically":[35],"some":[36],"undesirable":[37],"characteristics":[38],"for":[39],"particular":[40],"applications.":[41],"In":[42],"this":[43],"paper,":[44],"we":[45],"discuss":[46],"dependence":[47,122],"modeling":[48],"a":[50,57,72],"pattern":[51],"recognition":[52],"perspective":[53],"and":[54,105,120,131,139,173,188],"then":[55],"introduce":[56],"new":[58],"non-parametric":[59],"approach":[60],"based":[61,75],"on":[62,76],"anomaly":[63],"detection":[64,154],"through":[65],"cluster":[66],"analysis.":[67],"The":[68,94],"proposed":[69,143],"methodology":[70],"uses":[71],"weighting":[73],"procedure":[74,128],"Voronoi":[77,82],"cells":[78],"densities,":[79],"named":[80],"Weighted":[81],"Distance":[83],"(WVD),":[84],"identify":[86],"potentially":[87],"atypical":[88],"univariate":[91],"time":[92,100],"series.":[93],"advantages":[95],"are":[96],"two-fold.":[97],"First,":[98],"series":[101],"structure":[102],"is":[103,110,124,129,149],"respected":[104],"neither":[106],"independence":[107],"nor":[108],"homoscedasticity":[109],"presumed":[111],"within":[112],"data.":[113],"Second,":[114],"any":[115,121],"distribution":[116],"data":[119,148],"function":[123],"allowed.":[125],"An":[126],"inference":[127],"presented":[130],"simulation":[132],"studies":[133],"help":[134],"visualize":[136],"behavior":[138],"benefits":[140],"measure.":[144],"Finally,":[145],"real":[146],"financial":[147,161],"analyze":[152],"capacity":[155],"contagion":[158],"effect":[159],"in":[160],"markets":[162],"during":[163],"2007":[165],"sub-prime":[166],"crisis.":[167],"Different":[168],"asset":[169],"classes":[170],"were":[171],"included,":[172],"WVD":[175],"was":[176],"able":[177],"signalize":[179],"anomalies":[180],"more":[181],"strongly":[182],"than":[183],"Extreme":[185],"Value":[186],"Theory":[187],"approach.":[190]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}