State-dependent importance sampling schemes via minimum cross-entropy

Ad Ridder () and Thomas Taimre ()

Annals of Operations Research, 2011, vol. 189, issue 1, 357-388

Abstract: We present a method to obtain state- and time-dependent importance sampling estimators by repeatedly solving a minimum cross-entropy (MCE) program as the simulation progresses. This MCE-based approach lends a foundation to the natural notion to stop changing the measure when it is no longer needed. We use this method to obtain a state- and time-dependent estimator for the one-tailed probability of a light-tailed i.i.d. sum that is logarithmically efficient in general and strongly efficient when the jumps are Gaussian. We go on to construct an estimator for the two-tailed problem which is shown to be similarly efficient. We consider minor variants of the algorithm obtained via MCE, and present some numerical comparisons between our algorithms and others from the literature. Copyright The Author(s) 2011

Keywords: Cross-entropy; Rare events; Importance sampling; State dependence (search for similar items in EconPapers)
Date: 2011
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DOI: 10.1007/s10479-009-0611-7

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