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Smoothing the Hill Estimator

Part of:Stochastic processesLimit theorems

Published online by Cambridge University Press:  01 July 2016

Sidney Resnick and
Cătălin Stărică
Sidney Resnick*
Affiliation:
Cornell University
Cătălin Stărică*
Affiliation:
Cornell University
*
∗ Postal address: School of Operations Research and Industrial Engineering, 223 Rhodes Hall, Cornell University, Ithaca, NY 14853-3801, USA.
∗ Postal address: School of Operations Research and Industrial Engineering, 223 Rhodes Hall, Cornell University, Ithaca, NY 14853-3801, USA.
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Abstract

For sequences of i.i.d. random variables whose common tail 1 – F is regularly varying at infinity wtih an unknown index –α < 0, it is well known that the Hill estimator is consistent for α–1 and usually asymptotically normally distributed. However, because the Hill estimator is a function of k = k(n), the number of upper order statistics used and which is only subject to the conditions k →∞, k/n → 0, its use in practice is problematic since there are few reliable guidelines about how to choose k. The purpose of this paper is to make the use of the Hill estimator more reliable through an averaging technique which reduces the asymptotic variance. As a direct result the range in which the smoothed estimator varies as a function of k decreases and the successful use of the esimator is made less dependent on the choice of k. A tail empirical process approach is used to prove the weak convergence of a process closely related to the Hill estimator. The smoothed version of the Hill estimator is a functional of the tail empirical process.

MSC classification

Primary:60F17: Functional limit theorems; invariance principles
Secondary:60G70: Extreme value theory; extremal processes

Information

Type
General Applied Probability
Information
Advances in Applied Probability , Volume 29 , Issue 1 , March 1997, pp. 271 - 293
Copyright
Copyright © Applied Probability Trust 1997 

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Footnotes

The work of S. Resnick is partially supported by NSF Grant DSM-DMS-9400535 at Cornell University.

The work of C. Stǎricǎ is supported by NSF Grant DMS-9400535 at Cornell University.

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