Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/87437
Authors: 
Botev, Zdravko
Ridder, Ad
Rojas-Nandayapa, Leonardo
Year of Publication: 
2013
Series/Report no.: 
Tinbergen Institute Discussion Paper 13-127/III
Abstract: 
The Cross Entropy method is a well-known adaptive importance sampling method for rare-event probability estimation, which requires estimating an optimal importance sampling density within a parametric class. In this article we estimate an optimal importance sampling density within a wider semiparametric class of distributions. We show that this semiparametric version of the Cross Entropy method frequently yields efficient estimators. We illustrate the excellent practical performance of the method with numerical experiments and show that for the problems we consider it typically outperforms alternative schemes by orders of magnitude.
Subjects: 
Light-Tailed
Regularly-Varying
Subexponential
Rare-Event Probability
Cross Entropy method
Markov Chain Monte Carlo
JEL: 
C61
C63
Document Type: 
Working Paper

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