Asymptotic behavior of the Empirical Process for Gaussian data presenting seasonal long-memory
ESAIM: Probability and Statistics (2010)
- Volume: 6, page 293-309
- ISSN: 1292-8100
Access Full Article
topAbstract
topHow to cite
topHaye, Mohamedou Ould. "Asymptotic behavior of the Empirical Process for Gaussian data presenting seasonal long-memory." ESAIM: Probability and Statistics 6 (2010): 293-309. <http://eudml.org/doc/104294>.
@article{Haye2010,
abstract = {
We study the asymptotic behavior of the empirical process when the
underlying data are Gaussian and exhibit seasonal
long-memory. We prove that the limiting process can be quite
different from the limit obtained in the case of regular
long-memory. However, in both cases, the limiting process is
degenerated. We apply our results to von–Mises functionals and
U-Statistics.
},
author = {Haye, Mohamedou Ould},
journal = {ESAIM: Probability and Statistics},
keywords = {Empirical process; Hermite polynomials;
Rosenblatt processes; seasonal long-memory; U-statistics von–Mises functionals.},
language = {eng},
month = {3},
pages = {293-309},
publisher = {EDP Sciences},
title = {Asymptotic behavior of the Empirical Process for Gaussian data presenting seasonal long-memory},
url = {http://eudml.org/doc/104294},
volume = {6},
year = {2010},
}
TY - JOUR
AU - Haye, Mohamedou Ould
TI - Asymptotic behavior of the Empirical Process for Gaussian data presenting seasonal long-memory
JO - ESAIM: Probability and Statistics
DA - 2010/3//
PB - EDP Sciences
VL - 6
SP - 293
EP - 309
AB -
We study the asymptotic behavior of the empirical process when the
underlying data are Gaussian and exhibit seasonal
long-memory. We prove that the limiting process can be quite
different from the limit obtained in the case of regular
long-memory. However, in both cases, the limiting process is
degenerated. We apply our results to von–Mises functionals and
U-Statistics.
LA - eng
KW - Empirical process; Hermite polynomials;
Rosenblatt processes; seasonal long-memory; U-statistics von–Mises functionals.
UR - http://eudml.org/doc/104294
ER -
References
top- M.A. Arcones, Distributional limit theorems over a stationary Gaussian sequence of random vectors. Stochastic Process. Appl.88 (2000) 135-159.
- J.-M. Bardet, G. Lang, G. Oppenheim, A. Philippe and M.S. Taqqu, Generators of long-range processes: A survey, in Long range dependence: Theory and applications, edited by P. Doukhan, G. Oppenheim and M.S. Taqqu (to appear).
- P. Billingsley, Convergence of Probability measures. Wiley (1968).
- S. Csörgo and J. Mielniczuk, The empirical process of a short-range dependent stationary sequence under Gaussian subordination. Probab. Theory Related Fields104 (1996) 15-25.
- H. Dehling and M.S. Taqqu, The empirical process of some long-range dependent sequences with an application to U-statistics. Ann. Statist.4 (1989) 1767-1783.
- H. Dehling and M.S. Taqqu, Bivariate symmetric statistics of long-range dependent observations. J. Statist. Plann. Inference28 (1991) 153-165.
- R.L. Dobrushin and P. Major, Non central limit theorems for non-linear functionals of Gaussian fields. Z. Wahrsch. Verw. Geb.50 (1979) 27-52.
- P. Doukhan and S. Louhichi, A new weak dependence condition and applications to moment inequalities. Stochastic Process Appl.84 (1999) 313-342.
- P. Doukhan and D. Surgailis, Functional central limit theorem for the empirical process of short memory linear processes. C. R. Acad. Sci. Paris Sér. I Math.326 (1997) 87-92.
- J. Ghosh, A new graphical tool to detect non normality. J. Roy. Statist. Soc. Ser. B58 (1996) 691-702.
- L. Giraitis, Convergence of certain nonlinear transformations of a Gaussian sequence to self-similar process. Lithuanian Math. J.23 (1983) 58-68.
- L. Giraitis and R. Leipus, A generalized fractionally differencing approach in long-memory modeling. Lithuanian Math. J.35 (1995) 65-81.
- L. Giraitis and D. Surgailis Central limit theorem for the empirical process of a linear sequence with long memory. J. Statist. Plann. Inference 80 (1999) 81-93.
- I.S. Gradshteyn and I.M. Ryzhik, Table of integrals, series and products. Jeffrey A. 5th Edition. Academic Press (1994).
- H.C. Ho and T. Hsing, On the asymptotic expansion of the empirical process of long memory moving averages. Ann. Statist.24 (1996) 992-1024.
- I. Karatzas and S.E. Shreve, Brownian motion and stochastic calculus. Springer-Verlag, New York (1988).
- R. Leipus and M.-C. Viano, Modeling long-memory time series with finite or infinite variance: A general approach. J. Time Ser. Anal.21 (1997) 61-74.
- C. Newman, Asymptotic independence and limit theorems for positively and negatively dependent random variables. IMS Lecture Notes-Monographs Ser.5 (1984) 127-140.
- G. Oppenheim, M. Ould Haye and M.-C. Viano, Long memory with seasonal effects. Statist. Inf. Stoch. Proc.3 (2000) 53-68.
- M. Ould Haye, Longue mémoire saisonnière et convergence vers le processus de Rosenblatt. Pub. IRMA, Lille, 50-VIII (1999).
- M. Ould Haye, Asymptotic behavior of the empirical process for seasonal long-memory data. Pub. IRMA, Lille, 53-V (2000).
- M. Ould Haye and M.-C. Viano, Limit theorems under seasonal long-memory, in Long range dependence: Theory and applications, edited by P. Doukhan, G. Oppenheim and M.S. Taqqu (to appear).
- D.W. Pollard, Convergence of Stochastic Processes. Springer, New York (1984).
- M. Rosenblatt, Limit theorems for transformations of functionals of Gaussian sequences. Z. Wahrsch. Verw. Geb.55 (1981) 123-132.
- Q. Shao and H. Yu, Weak convergence for weighted empirical process of dependent sequences. Ann. Probab.24 (1996) 2094-2127.
- G.R. Shorack and J.A. Wellner, Empirical Processes with Applications to Statistics. Wiley, New York (1986).
- M.S. Taqqu, Weak convergence to fractional Brownian motion and to the Rosenblatt process. Z. Wahrsch. Verw. Geb.31 (1975) 287-302.
- A. Zygmund, Trigonometric Series. Cambridge University Press (1959).
NotesEmbed ?
topTo embed these notes on your page include the following JavaScript code on your page where you want the notes to appear.