New directions in Time Series Analysis (Guest Editor: Philippe Soulier)
Free Access
Issue
ESAIM: PS
Volume 6, 2002
New directions in Time Series Analysis (Guest Editor: Philippe Soulier)
Page(s) 239 - 258
Section New directions in Time Series Analysis (Guest Editor: Philippe Soulier)
DOI https://doi.org/10.1051/ps:2002013
Published online 15 November 2002
  1. T. Anderson, Goodness of fit tests for spectral distributions. Ann. Statist. 21 (1993) 830-847. [CrossRef] [MathSciNet]
  2. J.-M. Bardet, G. Lang, G. Oppenheim, A. Philippe and M. Taqqu, Generators of long-range dependent processes: A survey. Birkhäuser (2002).
  3. M. Bartlett, An introduction to stochastic processes. Cambridge University Press (1955).
  4. G. Box and D.A. Pierce, Distribution of residual autocorrelations in autoregressive-integrated moving average time series models. J. Am. Stat. Assoc. 65 (1970) 1509-1526. [CrossRef]
  5. P. Brockwell and R. Davis, Time Series: Theory and Methods. Springer-Verlag, Springer Ser. in Statistics (1991).
  6. W. Chen and R. Deo, A generalized portmanteau goodness-of-fit test for time series models. Preprint (2000).
  7. G. Fay, Théorèmes limites pour les fonctionnelles du périodogramme, Ph.D. Thesis. École Nationale Supérieure des Télécommunications (2000).
  8. G. Fay, E. Moulines and P. Soulier, Non linear functionals of the periodogram (submitted).
  9. G. Fay and P. Soulier, The periodogram of an i.i.d. sequence. Stochastic Process. Appl. 92 (2001) 315-343. [CrossRef] [MathSciNet]
  10. R. Fox and M. Taqqu, Large-sample properties of parameter estimates for strongly dependent stationary Gaussian time series. Ann. Statist. 14 (1986) 517-532. [CrossRef] [MathSciNet]
  11. L. Giraitis and D. Surgailis, A central limit theorem for quadratic forms in strongly dependent linear variables and its application to asymptotic normality of Whittles's estimate. Probab. Theory Related Fields 86 (1990) 87-104. [CrossRef] [MathSciNet]
  12. U. Grenander and M. Rosenblatt, Statistical analysis of stationary time series. Wiley, New York (1957).
  13. Y. Hosoya, A limit theory for long-range dependence and statistical inference on related models. Ann. Statist. 25 (1997) 105-137. [CrossRef] [MathSciNet]
  14. C. Hurvich, E. Moulines and P. Soulier, The FEXP estimator for potentially non-stationary linear time series. Stochastic Process. Appl. 97 (2002) 307-340. [CrossRef] [MathSciNet]
  15. C.W. Hurvich and W. Chen, An efficient taper for potentially overdifferenced long-memory time series. J. Time Ser. Anal. 21 (2000) 155-180. [CrossRef] [MathSciNet]
  16. D. Janas and R. von Sachs, Consistency for non-linear functions of the periodogram of tapered data. J. Time Ser. Anal. 16 (1995) 585-606. [CrossRef] [MathSciNet]
  17. C. Klueppelberg and T. Mikosch, The integrated periodogram for stable processes. Ann. Statist. 24 (1996) 1855-1879. [CrossRef] [MathSciNet]
  18. P. Kokoszka and T. Mikosch, The integrated periodogram for long-memory processes with finite or infinite variance. Stochastic Process. Appl. 66 (1997) 55-78. [CrossRef] [MathSciNet]
  19. H. Künsch, Discrimination between monotonic trends and long-range dependence. J. Appl. Probab. 23 (1986) 1025-1030. [CrossRef] [MathSciNet]
  20. T. Mikosch and R. Norvaisa, Uniform convergence of the empirical spectral distribution function. Stochastic Process. Appl. 70 (1997) 85-114. [CrossRef] [MathSciNet]
  21. A. Mokkadem, Une mesure d'information et son application à des tests pour les processus arma. C. R. Acad. Sci. Paris 319 (1994) 197-200.
  22. A. Mokkadem, A measure of information and its applications to test for randomness against ARMA alternatives and to goodness-of-fit test. Stochastic Process. Appl. 72 (1997) 145-159. [CrossRef] [MathSciNet]
  23. M. Taniguchi, On estimation of the integrals of certain functions of spectral density. J. Appl. Probab. 17 (1980) 73-83. [CrossRef] [MathSciNet]
  24. C. Velasco, Non-stationary log-periodogram regression. J. Econom. 91 (1999) 325-371. [CrossRef]
  25. Y. Yajima, Asymptotic properties of estimates in incorrect ARMA models for long-memory time series, in New directions in time series analysis. Part II. Proc. Workshop, Minneapolis/MN (USA) 1990. Springer, New York, IMA Vol. Math. Appl. 46 (1993) 375-382.

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.