{"id":"https://openalex.org/W2938749035","doi":"https://doi.org/10.1109/icassp.2019.8682828","title":"A Recursive Bayesian Model for Extreme Values","display_name":"A Recursive Bayesian Model for Extreme Values","publication_year":2019,"publication_date":"2019-04-17","ids":{"openalex":"https://openalex.org/W2938749035","doi":"https://doi.org/10.1109/icassp.2019.8682828","mag":"2938749035"},"language":"en","primary_location":{"id":"doi:10.1109/icassp.2019.8682828","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2019.8682828","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-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/A5058975744","display_name":"Douglas E. Johnston","orcid":"https://orcid.org/0000-0002-6450-5230"},"institutions":[{"id":"https://openalex.org/I177340980","display_name":"Farmingdale State College","ror":"https://ror.org/010thz337","country_code":"US","type":"education","lineage":["https://openalex.org/I177340980"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Douglas E. Johnston","raw_affiliation_strings":["Department of Applied Mathematics, Farmingdale State College, Farmingdale, N.Y., U.S.A"],"affiliations":[{"raw_affiliation_string":"Department of Applied Mathematics, Farmingdale State College, Farmingdale, N.Y., U.S.A","institution_ids":["https://openalex.org/I177340980"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5006962534","display_name":"Petar M. Djuri\u0107","orcid":"https://orcid.org/0000-0001-7791-3199"},"institutions":[{"id":"https://openalex.org/I59553526","display_name":"Stony Brook University","ror":"https://ror.org/05qghxh33","country_code":"US","type":"education","lineage":["https://openalex.org/I59553526"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Petar M. Djuric","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Stony Brook University, Stony Brook, N.Y., U.S.A"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Stony Brook University, Stony Brook, N.Y., U.S.A","institution_ids":["https://openalex.org/I59553526"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5058975744"],"corresponding_institution_ids":["https://openalex.org/I177340980"],"apc_list":null,"apc_paid":null,"fwci":0.7001,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.77229474,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":93,"max":97},"biblio":{"volume":"24","issue":null,"first_page":"5062","last_page":"5066"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10711","display_name":"Target Tracking and Data Fusion in Sensor Networks","score":0.9947999715805054,"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/T10711","display_name":"Target Tracking and Data Fusion in Sensor Networks","score":0.9947999715805054,"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/T10282","display_name":"Financial Risk and Volatility Modeling","score":0.9660000205039978,"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/T10876","display_name":"Fault Detection and Control Systems","score":0.9506999850273132,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6253572106361389},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5640864968299866},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.34610283374786377}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6253572106361389},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5640864968299866},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.34610283374786377}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp.2019.8682828","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2019.8682828","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W5365639","https://openalex.org/W602946569","https://openalex.org/W1499955083","https://openalex.org/W1924873506","https://openalex.org/W1973503202","https://openalex.org/W2017384700","https://openalex.org/W2046357557","https://openalex.org/W2085157190","https://openalex.org/W2097156736","https://openalex.org/W2101298354","https://openalex.org/W2116140670","https://openalex.org/W2130388432","https://openalex.org/W2131455269","https://openalex.org/W2150024051","https://openalex.org/W2160337655","https://openalex.org/W2161389011","https://openalex.org/W2294875987","https://openalex.org/W2341760625","https://openalex.org/W2497934016","https://openalex.org/W2502254989","https://openalex.org/W2802174541","https://openalex.org/W4232541684","https://openalex.org/W4251281949","https://openalex.org/W4255757734","https://openalex.org/W6640013882","https://openalex.org/W6678997558"],"related_works":["https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W2382290278","https://openalex.org/W2478288626","https://openalex.org/W4391913857","https://openalex.org/W2350741829","https://openalex.org/W2530322880"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3,66],"propose":[4],"a":[5,37,56,68,90,96],"new":[6,91],"approach":[7],"for":[8],"analyzing":[9],"extreme":[10,28,43],"values":[11],"such":[12],"as":[13],"large":[14],"losses":[15],"in":[16,33],"financial":[17],"markets.":[18],"Our":[19],"goal":[20],"is":[21,83,95],"to":[22,46],"compute":[23],"the":[24,41,48,52,62,73,75,80,101],"predictive":[25,76],"distribution":[26,45],"of":[27,40,51,100,104],"events":[29],"that":[30,94],"are":[31],"clustered":[32],"time.":[34],"We":[35,88],"apply":[36],"stochastic":[38],"parametrization":[39],"generalized":[42],"value":[44],"model":[47],"asymptotic":[49],"behavior":[50],"block-maximum":[53],"and":[54,65,106,114],"derive":[55,67],"Rao-Blackwellized":[57],"particle":[58],"filter.":[59],"This":[60],"reduces":[61],"parameter":[63],"space,":[64],"concise,":[69],"recursive":[70],"solution.":[71],"Using":[72],"filter,":[74],"distribution,":[77],"conditioned":[78],"on":[79],"past":[81],"data,":[82],"computed":[84],"at":[85],"each":[86],"sample-time.":[87],"introduce":[89],"risk-measure,":[92],"pVaR\u03b1,":[93],"more":[97],"robust":[98],"estimate":[99],"true":[102],"nature":[103],"value-at-risk,":[105],"illustrate":[107],"our":[108],"results":[109],"using":[110],"both":[111],"simulated":[112],"data":[113],"actual":[115],"stock":[116],"market":[117],"returns":[118],"from":[119],"1928-2017.":[120]},"counts_by_year":[{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}