Wavelet estimation of the long memory parameter for Hermite polynomial of Gaussian processes

Abstract : We consider stationary processes with long memory which are non-Gaussian and represented as Hermite polynomials of a Gaussian process. We focus on the corresponding wavelet coefficients and study the asymptotic behavior of the sum of their squares since this sum is often used for estimating the long-memory parameter. We show that the limit is not Gaussian but can be expressed using the non-Gaussian Rosenblatt process defined as a Wiener Itô integral of order 2. This happens even if the original process is defined through a Hermite polynomial of order higher than 2.
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ESAIM: Probability and Statistics, EDP Sciences, 2014, 18, pp.42-76. 〈10.1051/ps/2012026〉
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Dernière modification le : mardi 3 juillet 2018 - 11:47:53
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Marianne Clausel, François Roueff, Murad Taqqu, Ciprian Tudor. Wavelet estimation of the long memory parameter for Hermite polynomial of Gaussian processes. ESAIM: Probability and Statistics, EDP Sciences, 2014, 18, pp.42-76. 〈10.1051/ps/2012026〉. 〈hal-00590798v3〉

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