The Annals of Statistics

Adaptive Estimates of Parameters of Regular Variation

Peter Hall and A. H. Welsh

Full-text: Open access

Abstract

The problem of estimating shape and scale parameters for a distribution with regularly varying tails is related to that of nonparametrically estimating a density at a fixed point, in that optimal construction of the estimators depends substantially upon unknown features of the distribution. We show how to overcome this problem by using adaptive methods. Our main results hold very generally, for a large class of adaptive estimators. Later we consider specific versions of adaptive estimators, and describe their performance both in theory and by means of simulation studies. We also examine a technique proposed by Hill (1975) for solving similar problems.

Article information

Source
Ann. Statist., Volume 13, Number 1 (1985), 331-341.

Dates
First available in Project Euclid: 12 April 2007

Permanent link to this document
https://projecteuclid.org/euclid.aos/1176346596

Digital Object Identifier
doi:10.1214/aos/1176346596

Mathematical Reviews number (MathSciNet)
MR773171

Zentralblatt MATH identifier
0605.62033

JSTOR
links.jstor.org

Subjects
Primary: 60G05: Foundations of stochastic processes
Secondary: 60E05: Distributions: general theory 62F35: Robustness and adaptive procedures 60F17: Functional limit theorems; invariance principles

Keywords
Adaptive estimator order statistics regular variation scale parameter shape parameter stable laws

Citation

Hall, Peter; Welsh, A. H. Adaptive Estimates of Parameters of Regular Variation. Ann. Statist. 13 (1985), no. 1, 331--341. doi:10.1214/aos/1176346596. https://projecteuclid.org/euclid.aos/1176346596


Export citation