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Wavelet estimation of the long memory parameter for Hermitepolynomial of Gaussian processes∗∗∗

Published online by Cambridge University Press:  28 November 2013

M. Clausel ,
F. Roueff ,
M.S. Taqqu  and
C. Tudor
M. Clausel
Affiliation:
Laboratoire Jean Kuntzmann, Université de Grenoble, CNRS, 38041 Grenoble Cedex 9. France. marianne.clausel@imag.fr
F. Roueff
Affiliation:
Institut Telecom, Telecom Paris, CNRS LTCI, 46 rue Barrault, 75634 Paris Cedex 13, France; roueff@telecom-paristech.fr
M.S. Taqqu
Affiliation:
Departement of Mathematics and Statistics, Boston University, Boston, MA 02215, USA; murad@math.bu.edu
C. Tudor
Affiliation:
Laboratoire Paul Painlevé, UMR 8524 du CNRS, Université Lille 1, 59655 Villeneuve d’Ascq, France. Associate member: SAMM, Université de Panthéon-Sorbonne Paris 1; Ciprian.Tudor@math.univ-lille1.fr
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Abstract

We consider stationary processes with long memory which are non-Gaussian and representedas Hermite polynomials of a Gaussian process. We focus on the corresponding waveletcoefficients and study the asymptotic behavior of the sum of their squares since this sumis often used for estimating the long–memory parameter. We show that the limit is notGaussian but can be expressed using the non-Gaussian Rosenblatt process defined as aWiener–Itô integral of order 2. This happens even if the original process is definedthrough a Hermite polynomial of order higher than 2.

Keywords

Hermite processeswavelet coefficientswiener chaosself-similar processeslong–range dependence
Type
Research Article
Information
ESAIM: Probability and Statistics , Volume 18 , 2014 , pp. 42 - 76
Copyright
© EDP Sciences, SMAI, 2013

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