Wavelet estimation of the long memory parameter for Hermite polynomial of gaussian processes
ESAIM: Probability and Statistics, Tome 18 (2014), pp. 42-76.

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.

DOI : 10.1051/ps/2012026
Classification : 42C40, 60G18, 62M15, 60G20, 60G22
Mots-clés : Hermite processes, wavelet coefficients, wiener chaos, self-similar processes, long-range dependence
@article{PS_2014__18__42_0,
     author = {Clausel, M. and Roueff, F. and Taqqu, M. S. and Tudor, C.},
     title = {Wavelet estimation of the long memory parameter for {Hermite} polynomial of gaussian processes},
     journal = {ESAIM: Probability and Statistics},
     pages = {42--76},
     publisher = {EDP-Sciences},
     volume = {18},
     year = {2014},
     doi = {10.1051/ps/2012026},
     mrnumber = {3143733},
     zbl = {1310.42023},
     language = {en},
     url = {http://www.numdam.org/articles/10.1051/ps/2012026/}
}
TY  - JOUR
AU  - Clausel, M.
AU  - Roueff, F.
AU  - Taqqu, M. S.
AU  - Tudor, C.
TI  - Wavelet estimation of the long memory parameter for Hermite polynomial of gaussian processes
JO  - ESAIM: Probability and Statistics
PY  - 2014
SP  - 42
EP  - 76
VL  - 18
PB  - EDP-Sciences
UR  - http://www.numdam.org/articles/10.1051/ps/2012026/
DO  - 10.1051/ps/2012026
LA  - en
ID  - PS_2014__18__42_0
ER  - 
%0 Journal Article
%A Clausel, M.
%A Roueff, F.
%A Taqqu, M. S.
%A Tudor, C.
%T Wavelet estimation of the long memory parameter for Hermite polynomial of gaussian processes
%J ESAIM: Probability and Statistics
%D 2014
%P 42-76
%V 18
%I EDP-Sciences
%U http://www.numdam.org/articles/10.1051/ps/2012026/
%R 10.1051/ps/2012026
%G en
%F PS_2014__18__42_0
Clausel, M.; Roueff, F.; Taqqu, M. S.; Tudor, C. Wavelet estimation of the long memory parameter for Hermite polynomial of gaussian processes. ESAIM: Probability and Statistics, Tome 18 (2014), pp. 42-76. doi : 10.1051/ps/2012026. http://www.numdam.org/articles/10.1051/ps/2012026/

[1] P. Abry and V. Pipiras, Wavelet-based synthesis of the Rosenblatt process. Eurasip Signal Processing 86 (2006) 2326-2339. | Zbl

[2] P. Abry and D. Veitch, Wavelet analysis of long-range-dependent traffic. IEEE Trans. Inform. Theory 44 (1998) 2-15. | MR | Zbl

[3] P. Abry, D. Veitch and P. Flandrin, Long-range dependence: revisiting aggregation with wavelets. J. Time Ser. Anal. 19 (1998) 253-266. ISSN 0143-9782. | MR | Zbl

[4] P. Abry, Helgason H. and V. Pipiras, Wavelet-based analysis of non-Gaussian long-range dependent processes and estimation of the Hurst parameter. Lithuanian Math. J. 51 (2011) 287-302. | MR | Zbl

[5] J.-M. Bardet, Statistical study of the wavelet analysis of fractional Brownian motion. IEEE Trans. Inform. Theory 48 (2002) 991-999. | MR | Zbl

[6] J.-M. Bardet and C.A. Tudor, A wavelet analysis of the Rosenblatt process: chaos expansion and estimation of the self-similarity parameter. Stochastic Process. Appl. 120 (2010) 2331-2362. | MR | Zbl

[7] J.-M. Bardet, G. Lang, E. Moulines and P. Soulier, Wavelet estimator of long-range dependent processes. 19th “Rencontres Franco-Belges de Statisticiens” (Marseille, 1998). Stat. Inference Stoch. Process. 3 (2000) 85-99. | MR | Zbl

[8] J.M. Bardet, H. Bibi and A. Jouini, Adaptive wavelet based estimator of the memory parameter for stationary gaussian processes. Bernoulli 14 (2008) 691-724. | MR | Zbl

[9] J.-C. Breton and I. Nourdin, Error bounds on the non-normal approximation of hermite power variations of fractional brownian motion. Electron. Commun. Probab. 13 (2008) 482-493. | EuDML | MR | Zbl

[10] A. Chronopoulou, C. Tudor and F. Viens, Self-similarity parameter estimation and reproduction property for non-gaussian Hermite processes. Commun. Stoch. Anal. 5 (2011) 161-185. | MR

[11] M. Clausel, F. Roueff, M.S. Taqqu and C. Tudor, Large scale behavior of wavelet coefficients of non-linear subordinated processes with long memory. Appl. Comput. Harmonic Anal. 32 (2012) 223-241. | MR | Zbl

[12] M. Clausel, F. Roueff, M.S. Taqqu and C. Tudor, High order chaotic limits of wavelet scalograms under long-range dependence. Technical report, Hal-Institut Telecom (2012). http://hal-institut-telecom.archives-ouvertes.fr/hal-00662317. | MR | Zbl

[13] R.L. Dobrushin and P. Major, Non-central limit theorems for nonlinear functionals of Gaussian fields. Z. Wahrsch. Verw. Gebiete 50 (1979) 27-52. | MR | Zbl

[14] P. Embrechts and M. Maejima, Selfsimilar processes. Princeton University Press, Princeton, New York (2002). | MR | Zbl

[15] P. Flandrin, On the spectrum of fractional Brownian motions. IEEE Trans. Inform. Theory IT-35 (1989) 197-199. | MR

[16] P. Flandrin, Some aspects of nonstationary signal processing with emphasis on time-frequency and time-scale methods. Edited by J.M. Combes, A. Grossman and Ph. Tchamitchian, Wavelets. Springer-Verlag (1989) 68-98. | MR | Zbl

[17] P. Flandrin, Fractional Brownian motion and wavelets. Edited by M. Farge, J.C.R. Hung and J.C. Vassilicos, Fractals and Fourier Transforms-New Developments and New Applications. Oxford University Press (1991). | MR | Zbl

[18] P. Flandrin, Time-Frequency/Time-scale Analysis, 1st edition. Academic Press (1999). | MR | Zbl

[19] R. Fox and M.S. Taqqu. Large-sample properties of parameter estimates for strongly dependent stationary Gaussian time series. Ann. Statist. 14 (1986) 517-532. | MR | Zbl

[20] L. Giraitis and D. Surgailis, Central limit theorems and other limit theorems for functionals of gaussian processes. Z. Wahrsch. verw. Gebiete 70 (1985) 191-212. | MR | Zbl

[21] L. Giraitis and M.S. Taqqu, Whittle estimator for finite-variance non-gaussian time series with long memory. Ann. Statist. 27 (1999) 178-203. | MR | Zbl

[22] A.J. Lawrance and N.T. Kottegoda, Stochastic modelling of riverflow time series. J. Roy. Statist. Soc. Ser. A 140 (1977) 1-47.

[23] P. Major, Multiple Wiener-Itô integrals, vol. 849 of Lect. Notes Math. Springer, Berlin (1981). | MR | Zbl

[24] E. Moulines, F. Roueff and M.S. Taqqu, On the spectral density of the wavelet coefficients of long memory time series with application to the log-regression estimation of the memory parameter. J. Time Ser. Anal. 28 (2007) 155-187. | MR | Zbl

[25] I. Nourdin and G. Peccati, Stein's method meets Malliavin calculus: a short survey with new estimates. Technical report, Recent Advances in Stochastic Dynamics and Stochastic Analysis 8 (2010) 207-236. | MR | Zbl

[26] I. Nourdin and G. Peccati, Stein's method on wiener chaos. Probability Theory and Related Fields 154 (2009) 75-118. | MR | Zbl

[27] D. Nualart, The Malliavin Calculus and Related Topics. Springer (2006). | MR | Zbl

[28] P.M. Robinson, Log-periodogram regression of time series with long range dependence. Ann. Statist. 23 (1995) 1048-1072. | MR | Zbl

[29] P.M. Robinson, Gaussian semiparametric estimation of long range dependence. Ann. Statist. 23 (1995) 1630-1661. | MR | Zbl

[30] F. Roueff and M. S. Taqqu, Central limit theorems for arrays of decimated linear processes. Stoch. Proc. Appl. 119 (2009) 3006-3041. | MR | Zbl

[31] F. Roueff and M.S. Taqqu, Asymptotic normality of wavelet estimators of the memory parameter for linear processes. J. Time Ser. Anal. 30 (2009) 534-558. | MR | Zbl

[32] A. Scherrer, Analyses statistiques des communications sur puce. Ph.D. thesis, École normale supérieure de Lyon (2006). Available on http://www.ens-lyon.fr/LIP/Pub/Rapports/PhD/PhD2006/PhD2006-09.pdf.

[33] M.S. Taqqu, A representation for self-similar processes. Stoch. Proc. Appl. 7 (1978) 55-64. | MR | Zbl

[34] M.S. Taqqu, Central limit theorems and other limit theorems for functionals of gaussian processes. Z. Wahrsch. verw. Gebiete 70 (1979) 191-212. | Zbl

[35] G.W. Wornell and A.V. Oppenheim, Estimation of fractal signals from noisy measurements using wavelets. IEEE Trans. Signal Process. 40 (1992) 611-623.

Cité par Sources :