{"id":"https://openalex.org/W3143561742","doi":"https://doi.org/10.1109/wsc.2011.6147781","title":"Efficient rare event simulation for heavy-tailed systems via cross entropy","display_name":"Efficient rare event simulation for heavy-tailed systems via cross entropy","publication_year":2011,"publication_date":"2011-12-01","ids":{"openalex":"https://openalex.org/W3143561742","doi":"https://doi.org/10.1109/wsc.2011.6147781","mag":"3143561742"},"language":"en","primary_location":{"id":"doi:10.1109/wsc.2011.6147781","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wsc.2011.6147781","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2011 Winter Simulation Conference (WSC)","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/A5011147039","display_name":"Jos\u00e9 Blanchet","orcid":"https://orcid.org/0000-0001-5895-0912"},"institutions":[{"id":"https://openalex.org/I78577930","display_name":"Columbia University","ror":"https://ror.org/00hj8s172","country_code":"US","type":"education","lineage":["https://openalex.org/I78577930"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Jose Blanchet","raw_affiliation_strings":["IEOR Department, Columbia University, New York, NY, USA"],"affiliations":[{"raw_affiliation_string":"IEOR Department, Columbia University, New York, NY, USA","institution_ids":["https://openalex.org/I78577930"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5020152768","display_name":"Yixi Shi","orcid":null},"institutions":[{"id":"https://openalex.org/I78577930","display_name":"Columbia University","ror":"https://ror.org/00hj8s172","country_code":"US","type":"education","lineage":["https://openalex.org/I78577930"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yixi Shi","raw_affiliation_strings":["IEOR Department, Columbia University, New York, NY, USA"],"affiliations":[{"raw_affiliation_string":"IEOR Department, Columbia University, New York, NY, USA","institution_ids":["https://openalex.org/I78577930"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5011147039"],"corresponding_institution_ids":["https://openalex.org/I78577930"],"apc_list":null,"apc_paid":null,"fwci":1.6389,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.86104743,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"516","last_page":"527"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11720","display_name":"Probability and Risk Models","score":0.9997000098228455,"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"}},"topics":[{"id":"https://openalex.org/T11720","display_name":"Probability and Risk Models","score":0.9997000098228455,"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"}},{"id":"https://openalex.org/T12011","display_name":"Insurance, Mortality, Demography, Risk Management","score":0.9962000250816345,"subfield":{"id":"https://openalex.org/subfields/3317","display_name":"Demography"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10282","display_name":"Financial Risk and Volatility Modeling","score":0.9936000108718872,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/cross-entropy-method","display_name":"Cross-entropy method","score":0.7266228199005127},{"id":"https://openalex.org/keywords/weibull-distribution","display_name":"Weibull distribution","score":0.6085965633392334},{"id":"https://openalex.org/keywords/cross-entropy","display_name":"Cross entropy","score":0.6053714752197266},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.5927414298057556},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5786246657371521},{"id":"https://openalex.org/keywords/parametric-statistics","display_name":"Parametric statistics","score":0.5686336159706116},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.5213354825973511},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.48879119753837585},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.46563735604286194},{"id":"https://openalex.org/keywords/variance-reduction","display_name":"Variance reduction","score":0.4595223367214203},{"id":"https://openalex.org/keywords/importance-sampling","display_name":"Importance sampling","score":0.45841264724731445},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.41497549414634705},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.34941503405570984},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3289353847503662},{"id":"https://openalex.org/keywords/principle-of-maximum-entropy","display_name":"Principle of maximum entropy","score":0.21182510256767273},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.19836121797561646},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.19493889808654785},{"id":"https://openalex.org/keywords/optimization-problem","display_name":"Optimization problem","score":0.15936991572380066},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.12805309891700745}],"concepts":[{"id":"https://openalex.org/C75782508","wikidata":"https://www.wikidata.org/wiki/Q3333633","display_name":"Cross-entropy method","level":4,"score":0.7266228199005127},{"id":"https://openalex.org/C173291955","wikidata":"https://www.wikidata.org/wiki/Q732332","display_name":"Weibull distribution","level":2,"score":0.6085965633392334},{"id":"https://openalex.org/C167981619","wikidata":"https://www.wikidata.org/wiki/Q1685498","display_name":"Cross entropy","level":3,"score":0.6053714752197266},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.5927414298057556},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5786246657371521},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.5686336159706116},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.5213354825973511},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.48879119753837585},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.46563735604286194},{"id":"https://openalex.org/C62644790","wikidata":"https://www.wikidata.org/wiki/Q3454689","display_name":"Variance reduction","level":3,"score":0.4595223367214203},{"id":"https://openalex.org/C52740198","wikidata":"https://www.wikidata.org/wiki/Q1539564","display_name":"Importance sampling","level":3,"score":0.45841264724731445},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.41497549414634705},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.34941503405570984},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3289353847503662},{"id":"https://openalex.org/C9679016","wikidata":"https://www.wikidata.org/wiki/Q1417473","display_name":"Principle of maximum entropy","level":2,"score":0.21182510256767273},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.19836121797561646},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.19493889808654785},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.15936991572380066},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.12805309891700745},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C98036226","wikidata":"https://www.wikidata.org/wiki/Q7268356","display_name":"Quadratic assignment problem","level":3,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/wsc.2011.6147781","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wsc.2011.6147781","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2011 Winter Simulation Conference (WSC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W1986635170","https://openalex.org/W2019055161","https://openalex.org/W2031575583","https://openalex.org/W2058702345","https://openalex.org/W2159540136","https://openalex.org/W3143561742","https://openalex.org/W4211042066","https://openalex.org/W4253187718","https://openalex.org/W6646836043","https://openalex.org/W6658606897","https://openalex.org/W6665043060"],"related_works":["https://openalex.org/W1980187201","https://openalex.org/W3122506910","https://openalex.org/W3121862329","https://openalex.org/W2952397822","https://openalex.org/W4242404444","https://openalex.org/W3125926705","https://openalex.org/W2164777412","https://openalex.org/W2799238264","https://openalex.org/W1981604164","https://openalex.org/W2134519345"],"abstract_inverted_index":{"The":[0,53],"cross":[1],"entropy":[2],"method":[3,41],"is":[4,59],"a":[5,23,46,76,122],"popular":[6],"technique":[7],"that":[8],"has":[9],"been":[10],"used":[11],"in":[12,19,74,84,102,141],"the":[13,27,43,56,63,67,82,85,93,105],"context":[14],"of":[15,29,34,45,55,66,81,125],"rare":[16],"event":[17],"simulation":[18],"order":[20],"to":[21,50,100,137],"obtain":[22,111],"good":[24],"selection":[25,44,54],"(in":[26],"sense":[28],"variance":[30],"performance":[31],"tested":[32],"empirically)":[33],"an":[35],"importance":[36,107],"sampling":[37,108],"distribution.":[38,109],"This":[39],"iterative":[40],"requires":[42],"suitable":[47],"parametric":[48,57,94,112],"family":[49,58,95],"start":[51],"with.":[52],"very":[60],"important":[61],"for":[62,114,121],"successful":[64],"application":[65],"method.":[68],"Two":[69],"properties":[70,118],"must":[71,87],"be":[72,88,97,138],"enforced":[73],"such":[75],"selection.":[77],"First,":[78],"subsequent":[79],"updates":[80],"parameters":[83],"iterations":[86],"easily":[89],"computable":[90],"and,":[91],"second,":[92],"should":[96],"powerful":[98],"enough":[99],"approximate,":[101],"some":[103],"sense,":[104],"zero-variance":[106],"We":[110],"families":[113],"which":[115],"these":[116,142],"two":[117],"are":[119,135],"satisfied":[120],"large":[123],"class":[124],"heavy-tailed":[126],"systems":[127],"including":[128],"Pareto":[129],"and":[130],"Weibull":[131],"tails.":[132],"Our":[133],"estimators":[134],"shown":[136],"strongly":[139],"efficient":[140],"settings.":[143]},"counts_by_year":[{"year":2012,"cited_by_count":1}],"updated_date":"2025-11-25T21:42:39.735039","created_date":"2025-10-10T00:00:00"}
