Variance Reduction Techniques for Estimating Value-at-Risk
Management Science
Importance sampling for sums of random variables with regularly varying tails
ACM Transactions on Modeling and Computer Simulation (TOMACS)
On Sums of Conditionally Independent Subexponential Random Variables
Mathematics of Operations Research
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In this article, we consider a Gaussian random field f(t) living on a compact set T⊂ Rd and the computation of the tail probabilities P(∫Tef(t)dt eb) as b → ∞. We design asymptotically efficient importance sampling estimators for a general class of Hölder continuous Gaussian random fields. In addition to the variance control, we also analyze the bias (relative to the interesting tail probabilities) caused by the discretization.