Improved cross-entropy method for estimation

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dc.contributor.authorChan, Chi Chun (Joshua)
dc.contributor.authorKroese, Dirk
dc.date.accessioned2015-12-08T22:45:02Z
dc.date.issued2012
dc.date.updated2016-02-24T12:00:29Z
dc.description.abstractThe cross-entropy (CE) method is an adaptive importance sampling procedure that has been successfully applied to a diverse range of complicated simulation problems. However, recent research has shown that in some high-dimensional settings, the likelihood
dc.identifier.issn0960-3174
dc.identifier.urihttp://hdl.handle.net/1885/37656
dc.publisherKluwer Academic Publishers
dc.sourceStatistics and Computing
dc.subjectKeywords: Cross-entropy; Importance sampling; Kullback-Leibler divergence; Likelihood ratio degeneracy; Rare-event simulation; t copula; Variance minimization
dc.titleImproved cross-entropy method for estimation
dc.typeJournal article
local.bibliographicCitation.issueNo. 5
local.bibliographicCitation.lastpage1040
local.bibliographicCitation.startpage1031
local.contributor.affiliationChan, Chi Chun (Joshua), College of Business and Economics, ANU
local.contributor.affiliationKroese, Dirk, University of Queensland
local.contributor.authoremailu4935553@anu.edu.au
local.contributor.authoruidChan, Chi Chun (Joshua), u4935553
local.description.embargo2037-12-31
local.description.notesImported from ARIES
local.identifier.absfor140302 - Econometric and Statistical Methods
local.identifier.absseo910299 - Microeconomics not elsewhere classified
local.identifier.ariespublicationU9501697xPUB151
local.identifier.citationvolume22
local.identifier.doi10.1007/s11222-011-9275-7
local.identifier.scopusID2-s2.0-84863556279
local.identifier.thomsonID000305963700004
local.identifier.uidSubmittedByU9501697
local.type.statusPublished Version

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