Improved cross-entropy method for estimation

Chan, JCC; Kroese, DP

Improved cross-entropy method for estimation

Chan, JCC

Kroese, DP

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Publication Type:: Journal Article
Citation:: Statistics and Computing, 2012, 22 (5), pp. 1031 - 1040
Issue Date:: 2012-09-01

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Field	Value	Language
dc.contributor.author	Chan, JCC https://orcid.org/0000-0003-3632-128X	en_US
dc.contributor.author	Kroese, DP	en_US
dc.date.issued	2012-09-01	en_US
dc.identifier.citation	Statistics and Computing, 2012, 22 (5), pp. 1031 - 1040	en_US
dc.identifier.issn	0960-3174	en_US
dc.identifier.uri	http://hdl.handle.net/10453/115040
dc.description.abstract	The 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 ratio degeneracy problem becomes severe and the importance sampling estimator obtained from the CE algorithm becomes unreliable. We consider a variation of the CE method whose performance does not deteriorate as the dimension of the problem increases. We then illustrate the algorithm via a high-dimensional estimation problem in risk management. © 2011 Springer Science+Business Media, LLC.	en_US
dc.relation.ispartof	Statistics and Computing	en_US
dc.relation.isbasedon	10.1007/s11222-011-9275-7	en_US
dc.subject.classification	Statistics & Probability	en_US
dc.title	Improved cross-entropy method for estimation	en_US
dc.type	Journal Article
utslib.citation.volume	5	en_US
utslib.citation.volume	22	en_US
utslib.for	0104 Statistics	en_US
utslib.for	0802 Computation Theory and Mathematics	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Business
pubs.organisational-group	/University of Technology Sydney/Faculty of Business/Economics Discipline
utslib.copyright.status	closed_access
pubs.issue	5	en_US
pubs.publication-status	Published	en_US
pubs.volume	22	en_US

Abstract:

The 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 ratio degeneracy problem becomes severe and the importance sampling estimator obtained from the CE algorithm becomes unreliable. We consider a variation of the CE method whose performance does not deteriorate as the dimension of the problem increases. We then illustrate the algorithm via a high-dimensional estimation problem in risk management. © 2011 Springer Science+Business Media, LLC.

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http://hdl.handle.net/10453/115040