Improved cross-entropy method for estimation

Publication Type:
Journal Article
Statistics and Computing, 2012, 22 (5), pp. 1031 - 1040
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10.1007%2Fs11222-011-9275-7.pdfPublished Version484.56 kB
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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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