EDP Sciences logo
Subscriber Authentication Point
Search
Menu
All issues Volume 6 (2002) ESAIM: PS, 6 (2002) 211-238 References
Issue
ESAIM: PS
Volume 6, 2002
New directions in Time Series Analysis (Guest Editor: Philippe Soulier)
Page(s) 211 - 238
Section New directions in Time Series Analysis (Guest Editor: Philippe Soulier)
DOI https://doi.org/10.1051/ps:2002012
Published online 15 November 2002
  1. G. Banon, Nonparametric identification for diffusion processes. SIAM J. Control Optim. 16 (1978) 380-395. [Google Scholar]
  2. G. Banon and H.T. N'Guyen, Recursive estimation in diffusion model. SIAM J. Control Optim. 19 (1981) 676-685. [CrossRef] [MathSciNet] [Google Scholar]
  3. A.R. Barron, L. Birgé and P. Massart, Risk bounds for model selection via penalization. Probab. Theory Related Fields 113 (1999) 301-413. [Google Scholar]
  4. H.C.P Berbee, Random walks with stationary increments and renewal theory. Cent. Math. Tracts, Amsterdam (1979). [Google Scholar]
  5. L. Birgé and P. Massart, From model selection to adaptive estimation, in Festschrift for Lucien Le Cam: Research Papers in Probability and Statistics, edited by D. Pollard, E. Torgersen and G. Yang. Springer-Verlag, New-York (1997) 55-87. [Google Scholar]
  6. L. Birgé and P. Massart, Minimum contrast estimators on sieves: Exponential bounds and rates of convergence. Bernoulli 4 (1998) 329-375. [CrossRef] [MathSciNet] [Google Scholar]
  7. L. Birgé and P. Massart, An adaptive compression algorithm in Besov spaces. Constr. Approx. 16 (2000) 1-36. [CrossRef] [MathSciNet] [Google Scholar]
  8. L. Birgé and Y. Rozenholc, How many bins must be put in a regular histogram? Preprint LPMA 721, http://www.proba.jussieu.fr/mathdoc/preprints/index.html (2002). [Google Scholar]
  9. D. Bosq, Parametric rates of nonparametric estimators and predictors for continuous time processes. Ann. Stat. 25 (1997) 982-1000. [CrossRef] [Google Scholar]
  10. D. Bosq, Nonparametric Statistics for Stochastic Processes. Estimation and Prediction, Second Edition. Springer Verlag, New-York, Lecture Notes in Statist. 110 (1998). [Google Scholar]
  11. D. Bosq and Yu. Davydov, Local time and density estimation in continuous time. Math. Methods Statist. 8 (1999) 22-45. [MathSciNet] [Google Scholar]
  12. W. Bryc, On the approximation theorem of Berkes and Philipp. Demonstratio Math. 15 (1982) 807-815. [MathSciNet] [Google Scholar]
  13. C. Butucea, Exact adaptive pointwise estimation on Sobolev classes of densities. ESAIM: P&S 5 (2001) 1-31. [CrossRef] [EDP Sciences] [Google Scholar]
  14. J.V. Castellana and M.R. Leadbetter, On smoothed probability density estimation for stationary processes. Stochastic Process. Appl. 21 (1986) 179-193. [CrossRef] [MathSciNet] [Google Scholar]
  15. S. Clémençon, Adaptive estimation of the transition density of a regular Markov chain. Math. Methods Statist. 9 (2000) 323-357. [MathSciNet] [Google Scholar]
  16. A. Cohen, I. Daubechies and P. Vial, Wavelet and fast wavelet transform on an interval. Appl. Comput. Harmon. Anal. 1 (1993) 54-81. [CrossRef] [MathSciNet] [Google Scholar]
  17. F. Comte and F. Merlevède, Density estimation for a class of continuous time or discretely observed processes. Preprint MAP5 2002-2, http://www.math.infor.univ-paris5.fr/map5/ (2002). [Google Scholar]
  18. F. Comte and Y. Rozenholc, Adaptive estimation of mean and volatility functions in (auto-)regressive models. Stochastic Process. Appl. 97 (2002) 111-145. [Google Scholar]
  19. I. Daubechies, Ten lectures on wavelets. SIAM: Philadelphia (1992). [Google Scholar]
  20. B. Delyon, Limit theorem for mixing processes, Technical Report IRISA. Rennes (1990) 546. [Google Scholar]
  21. R.A. DeVore and G.G. Lorentz, Constructive approximation. Springer-Verlag (1993). [Google Scholar]
  22. D.L. Donoho and I.M. Johnstone, Minimax estimation with wavelet shrinkage. Ann. Statist. 26 (1998) 879-921. [Google Scholar]
  23. D.L. Donoho, I.M. Johnstone, G. Kerkyacharian and D. Picard, Density estimation by wavelet thresholding. Ann. Statist. 24 (1996) 508-539. [Google Scholar]
  24. P. Doukhan, Mixing properties and examples. Springer-Verlag, Lecture Notes in Statist. (1995). [Google Scholar]
  25. Y. Efromovich, Nonparametric estimation of a density of unknown smoothness. Theory Probab. Appl. 30 (1985) 557-661. [CrossRef] [Google Scholar]
  26. Y. Efromovich and M.S. Pinsker, Learning algorithm for nonparametric filtering. Automat. Remote Control 11 (1984) 1434-1440. [Google Scholar]
  27. G. Kerkyacharian, D. Picard and K. Tribouley, Formula adaptive density estimation. Bernoulli 2 (1996) 229-247. [MathSciNet] [Google Scholar]
  28. A.N. Kolmogorov and Y.A. Rozanov, On the strong mixing conditions for stationary Gaussian sequences. Theory Probab. Appl. 5 (1960) 204-207. [CrossRef] [Google Scholar]
  29. Y.A. Kutoyants, Efficient density estimation for ergodic diffusion processes. Stat. Inference Stoch. Process. 1 (1998) 131-155. [CrossRef] [Google Scholar]
  30. F. Leblanc, Density estimation for a class of continuous time processes. Math. Methods Statist. 6 (1997) 171-199. [MathSciNet] [Google Scholar]
  31. H.T. N'Guyen, Density estimation in a continuous-time stationary Markov process. Ann. Statist. 7 (1979) 341-348. [CrossRef] [MathSciNet] [Google Scholar]
  32. E. Rio, The functional law of the iterated logarithm for stationary strongly mixing sequences. Ann. Probab. 23 (1995) 1188-1203. [CrossRef] [MathSciNet] [Google Scholar]
  33. M. Rosenblatt, A central limit theorem and a strong mixing condition. Proc. Nat. Acad. Sci. USA 42 (1956) 43-47. [Google Scholar]
  34. M. Talagrand, New concentration inequalities in product spaces. Invent. Math. 126 (1996) 505-563. [CrossRef] [MathSciNet] [Google Scholar]
  35. K. Tribouley and G. Viennet, Formula adaptive density estimation in a Formula -mixing framework. Ann. Inst. H. Poincaré 34 (1998) 179-208. [CrossRef] [MathSciNet] [Google Scholar]
  36. A.Yu. Veretennikov, On hypoellipticity conditions and estimates of the mixing rate for stochastic differential equations. Soviet Math. Dokl. 40 (1990) 94-97. [MathSciNet] [Google Scholar]
  37. G. Viennet, Inequalities for absolutely regular sequences: Application to density estimation. Probab. Theory Related Fields 107 (1997) 467-492. [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.