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A0986
Title: On the consistency of nonparametric bootstrap for inference on high-quantile, tail index, and tail probability Authors:  Svetlana Litvinova - Monash University and St. Petersburg State University (Australia) [presenting]
Mervyn Silvapulle - Monash University (Australia)
Abstract: The full-sample bootstrap is shown to be asymptotically valid for constructing confidence intervals for high-quantiles, tail probabilities, and other tail parameters of a univariate distribution. This resolves the doubts that have been raised about the validity of such bootstrap methods. In our extensive simulation study, the overall performance of the bootstrap method was better than that of the standard asymptotic method. The method of proof is likely to be useful for studying nonparametric bootstrap for inference about extreme tail events more generally.