On optimising the estimation of high quantiles of a probability distribution

  • Ferreira A
  • De Haan L
  • Peng L
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One of the major aims of one-dimensional extreme-value theory is to estimate quantiles outside the sample or at the boundary of the sample. The underlying idea of any method to do this is to estimate a quantile well inside the sample but near the boundary and then to shift it somehow to the right place. The choice of this “anchor quantile” plays a major role in the accuracy of the method. We present a bootstrap method to achieve the optimal choice of sample fraction in the estimation of either high quantile or endpoint estimation which extends earlier results by Hall and Weissman (1997) in the case of high quantile estimation. We give detailed results for the estimators used by Dekkers et al. (1989). An alternative way of attacking problems like this one is given in a paper by Drees and Kaufmann (1998).

Author-supplied keywords

  • Bootstrap
  • Endpoint estimation
  • Extreme-value theory
  • Generalized pareto distribution
  • Quantile estimation

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  • A. Ferreira

  • L. De Haan

  • L. Peng

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