{"id":"https://openalex.org/W3016254424","doi":"https://doi.org/10.1109/icassp40776.2020.9053302","title":"A Recursive Bayesian Solution for the Excess Over Threshold Distribution with Stochastic Parameters","display_name":"A Recursive Bayesian Solution for the Excess Over Threshold Distribution with Stochastic Parameters","publication_year":2020,"publication_date":"2020-04-09","ids":{"openalex":"https://openalex.org/W3016254424","doi":"https://doi.org/10.1109/icassp40776.2020.9053302","mag":"3016254424"},"language":"en","primary_location":{"id":"doi:10.1109/icassp40776.2020.9053302","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp40776.2020.9053302","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2020 - 2020 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, U.S.A"],"affiliations":[{"raw_affiliation_string":"Department of Applied Mathematics, Farmingdale State College, 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, U.S.A"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Stony Brook University, 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":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.06215228,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"8439","last_page":"8443"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10282","display_name":"Financial Risk and Volatility Modeling","score":0.9995999932289124,"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"}},"topics":[{"id":"https://openalex.org/T10282","display_name":"Financial Risk and Volatility Modeling","score":0.9995999932289124,"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/T10711","display_name":"Target Tracking and Data Fusion in Sensor Networks","score":0.9987000226974487,"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/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9976000189781189,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/conditional-probability-distribution","display_name":"Conditional probability distribution","score":0.5381506085395813},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.50960373878479},{"id":"https://openalex.org/keywords/posterior-predictive-distribution","display_name":"Posterior predictive distribution","score":0.49250468611717224},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.4748537838459015},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4740923345088959},{"id":"https://openalex.org/keywords/distribution","display_name":"Distribution (mathematics)","score":0.47083616256713867},{"id":"https://openalex.org/keywords/particle-filter","display_name":"Particle filter","score":0.44951558113098145},{"id":"https://openalex.org/keywords/generalized-pareto-distribution","display_name":"Generalized Pareto distribution","score":0.4179288148880005},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.4126646816730499},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.3971173167228699},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.3765687346458435},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3325542211532593},{"id":"https://openalex.org/keywords/extreme-value-theory","display_name":"Extreme value theory","score":0.31515586376190186},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.29346776008605957},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.28118908405303955},{"id":"https://openalex.org/keywords/kalman-filter","display_name":"Kalman filter","score":0.17269659042358398},{"id":"https://openalex.org/keywords/bayesian-linear-regression","display_name":"Bayesian linear regression","score":0.1646694839000702}],"concepts":[{"id":"https://openalex.org/C43555835","wikidata":"https://www.wikidata.org/wiki/Q2300258","display_name":"Conditional probability distribution","level":2,"score":0.5381506085395813},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.50960373878479},{"id":"https://openalex.org/C83247935","wikidata":"https://www.wikidata.org/wiki/Q7234227","display_name":"Posterior predictive distribution","level":5,"score":0.49250468611717224},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4748537838459015},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4740923345088959},{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.47083616256713867},{"id":"https://openalex.org/C52421305","wikidata":"https://www.wikidata.org/wiki/Q1151499","display_name":"Particle filter","level":3,"score":0.44951558113098145},{"id":"https://openalex.org/C133514767","wikidata":"https://www.wikidata.org/wiki/Q5532448","display_name":"Generalized Pareto distribution","level":3,"score":0.4179288148880005},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.4126646816730499},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3971173167228699},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3765687346458435},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3325542211532593},{"id":"https://openalex.org/C147581598","wikidata":"https://www.wikidata.org/wiki/Q729429","display_name":"Extreme value theory","level":2,"score":0.31515586376190186},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.29346776008605957},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.28118908405303955},{"id":"https://openalex.org/C157286648","wikidata":"https://www.wikidata.org/wiki/Q846780","display_name":"Kalman filter","level":2,"score":0.17269659042358398},{"id":"https://openalex.org/C37903108","wikidata":"https://www.wikidata.org/wiki/Q4874474","display_name":"Bayesian linear regression","level":4,"score":0.1646694839000702},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp40776.2020.9053302","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp40776.2020.9053302","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2020 - 2020 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":33,"referenced_works":["https://openalex.org/W5365639","https://openalex.org/W45903167","https://openalex.org/W1499955083","https://openalex.org/W1500657154","https://openalex.org/W1924873506","https://openalex.org/W1973503202","https://openalex.org/W2017384700","https://openalex.org/W2019199024","https://openalex.org/W2085157190","https://openalex.org/W2090663454","https://openalex.org/W2097156736","https://openalex.org/W2099352364","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/W2488562761","https://openalex.org/W2497934016","https://openalex.org/W2502254989","https://openalex.org/W2763204001","https://openalex.org/W2802174541","https://openalex.org/W2920992240","https://openalex.org/W2938749035","https://openalex.org/W3147291723","https://openalex.org/W4232541684","https://openalex.org/W4255757734","https://openalex.org/W4298193418","https://openalex.org/W6640013882","https://openalex.org/W6678997558"],"related_works":["https://openalex.org/W2948998474","https://openalex.org/W2621623949","https://openalex.org/W3104492748","https://openalex.org/W4389855300","https://openalex.org/W577488893","https://openalex.org/W1491417657","https://openalex.org/W2047947128","https://openalex.org/W2587066997","https://openalex.org/W2551444002","https://openalex.org/W3038575280"],"abstract_inverted_index":{"In":[0],"this":[1,73],"paper,":[2],"we":[3,47,91],"propose":[4],"a":[5,43,59,81,93],"new":[6],"approach":[7],"for":[8,52,97],"analyzing":[9],"extreme":[10,27],"values":[11],"that":[12,29],"are":[13,30],"witnessed":[14],"in":[15,32,142],"financial":[16],"markets.":[17],"Our":[18],"goal":[19],"is":[20,117],"to":[21,37,67],"compute":[22],"the":[23,40,49,53,63,69,87,98,101,104,106,110,114,134,137],"predictive":[24,107,146],"distribution":[25,66,108],"of":[26,42,45,62,72,100,109],"events":[28],"clustered":[31],"time":[33],"and,":[34],"as":[35],"opposed":[36],"modeling":[38],"just":[39],"maximum":[41,138],"block":[44],"observations,":[46],"model":[48,68,125],"conditional":[50,74],"tail":[51],"underlying":[54],"random":[55],"process.":[56],"We":[57,79,122],"apply":[58],"stochastic":[60],"parameterization":[61],"generalized":[64],"Pareto":[65],"asymptotic":[70],"behavior":[71],"tail,":[75],"or":[76],"excess":[77],"distribution.":[78,102],"utilize":[80],"Rao-Blackwellized":[82],"particle":[83],"filter,":[84,105],"which":[85,129],"reduces":[86],"parameter":[88,143],"space,":[89],"and":[90,136,145],"derive":[92],"concise,":[94],"recursive":[95],"solution":[96],"parameters":[99],"Using":[103],"parameters,":[111],"conditioned":[112],"on":[113,126],"past":[115],"data,":[116],"computed":[118],"at":[119],"each":[120],"sample-time.":[121],"test":[123],"our":[124],"simulated":[127],"data":[128],"show":[130],"an":[131],"improvement":[132],"over":[133],"block-maximum":[135],"likelihood":[139],"approaches":[140],"both":[141],"estimation":[144],"performance.":[147]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-11-25T21:42:39.735039","created_date":"2025-10-10T00:00:00"}