Improved cross-entropy method for estimation
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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.
KeywordsCross-entropy Variance minimization Importance sampling Kullback-Leibler divergence Rare-event simulation Likelihood ratio degeneracy t copula
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