Free Access
Issue |
ESAIM: PS
Volume 12, April 2008
|
|
---|---|---|
Page(s) | 154 - 172 | |
DOI | https://doi.org/10.1051/ps:2007053 | |
Published online | 23 January 2008 |
- D. Andrews, Non strong mixing autoregressive processes. J. Appl. Probab. 21 (1984) 930–934. [CrossRef] [MathSciNet] [Google Scholar]
- P. Billingsley, Convergence of Probability Measures. Wiley, New-York (1968). [Google Scholar]
- A.V. Bulinski and A.P. Shashkin, Rates in the central limit theorem for weakly dependent random variables. J. Math. Sci. 122 (2004) 3343–3358. [CrossRef] [MathSciNet] [Google Scholar]
- A.V. Bulinski and A.P. Shashkin, Strong Invariance Principle for Dependent Multi-indexed Random Variables. Doklady Mathematics 72 (2005) 503–506. [Google Scholar]
- C. Coulon-Prieur and P. Doukhan, A triangular central limit theorem under a new weak dependence condition. Stat. Prob. Letters 47 (2000) 61–68. [Google Scholar]
- P. Doukhan, Mixing: Properties and Examples. Lect. Notes Statis. 85 (1994). [Google Scholar]
- P. Doukhan, Models inequalities and limit theorems for stationary sequences, in Theory and applications of long range dependence, Doukhan et al. Ed., Birkhäuser (2003) 43–101. [Google Scholar]
- P. Doukhan and G. Lang, Rates in the empirical central limit theorem for stationary weakly dependent random fields. Stat. Inference Stoch. Process. 5 (2002) 199–228. [CrossRef] [MathSciNet] [Google Scholar]
- P. Doukhan and S. Louhichi, A new weak dependence condition and applications to moment inequalities. Stoch. Proc. Appl. 84 (1999) 313–342. [CrossRef] [MathSciNet] [Google Scholar]
- P. Doukhan, H. Madre and M. Rosenbaum, Weak dependence for infinite ARCH-type bilinear models. Statistics 41 (2007) 31–45. [CrossRef] [MathSciNet] [Google Scholar]
- P. Doukhan, G. Teyssiere and P. Winant, Vector valued ARCH(∞) processes, in Dependence in Probability and Statistics, P. Bertail, P. Doukhan and P. Soulier Eds. Lecture Notes in Statistics, Springer, New York (2006). [Google Scholar]
- P. Doukhan and O. Wintenberger, An invariance principle for weakly dependent stationary general models. Prob. Math. Stat. 27 (2007) 45–73. [Google Scholar]
- L. Giraitis and D. Surgailis, ARCH-type bilinear models with double long memory. Stoch. Proc. Appl. 100 (2002) 275–300. [CrossRef] [Google Scholar]
- M.H. Neumann and E. Paparoditis, Goodness-of-fit tests for Markovian time series models. Technical Report No. 16/2005. Department of Mathematics and Statistics, University of Cyprus (2005). [Google Scholar]
- V. Petrov, Limit theorems of probability theory. Clarendon Press, Oxford (1995). [Google Scholar]
- B.L.S. Prakasha Rao, Nonparametric functional estimation. Academic Press, New York (1983). [Google Scholar]
- E. Rio, About the Lindeberg method for strongly mixing sequences. ESAIM: PS 1 (1997) 35–61. [Google Scholar]
- E. Rio, Théorie asymptotique pour des processus aléatoires faiblement dépendants. SMAI, Math. Appl. 31 (2000). [Google Scholar]
- P.M. Robinson, Nonparametric estimators for time series. J. Time Ser. Anal. 4 (1983) 185–207. [CrossRef] [MathSciNet] [Google Scholar]
- M.S. Taqqu, Weak convergence to fractional Brownian motion and to the Rosenblatt process. Z. Wahrsch. Verw. Gebiete 31 (1975) 237–302. [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.