The Annals of Statistics

Note on convergence rates of semiparametric estimators of dependence index

Peter Hall, Hira L. Koul, and Berwin A. Turlach

Full-text: Open access

Abstract

Considerable recent attention has been devoted to semiparametric estimation of the dependence index, or the Hurst constant, using methods based on information in either frequency or time domains. Convergence rates of estimators in the frequency domain have been derived, and in the present paper we obtain them for estimators in the time domain. It is shown that the latter can have superior performance for moderate-range time series, but are inferior in the context of long-range dependence.

Article information

Source
Ann. Statist., Volume 25, Number 4 (1997), 1725-1739.

Dates
First available in Project Euclid: 9 September 2002

Permanent link to this document
https://projecteuclid.org/euclid.aos/1031594739

Digital Object Identifier
doi:10.1214/aos/1031594739

Mathematical Reviews number (MathSciNet)
MR1463572

Zentralblatt MATH identifier
0890.62068

Subjects
Primary: 62M10: Time series, auto-correlation, regression, etc. [See also 91B84]
Secondary: 62G05: Estimation

Keywords
Autocovariance Hurst constant long-memory time series long-range dependence semiparametric inference stationary process Gaussian process short-range dependence

Citation

Hall, Peter; Koul, Hira L.; Turlach, Berwin A. Note on convergence rates of semiparametric estimators of dependence index. Ann. Statist. 25 (1997), no. 4, 1725--1739. doi:10.1214/aos/1031594739. https://projecteuclid.org/euclid.aos/1031594739


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References

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  • AUSTRALIAN NATIONAL UNIVERSITY EAST LANSING, MICHIGAN 48824
  • CANBERRA, ACT 0200 AUSTRALIA E-MAIL: halpstat@durras.anu.edu.au