Free Access
Volume 5, 2001
Page(s) 33 - 49
Published online 15 August 2002
  1. H. Akaike, Information theory and an extension of the maximum likelihood principle, in Proc. 2nd International Symposium on Information Theory, edited by P.N. Petrov and F. Csaki. Akademia Kiado, Budapest (1973) 267-281. [Google Scholar]
  2. H. Akaike, A new look at the statistical model identification. IEEE Trans. Automat. Control 19 (1984) 716-723. [Google Scholar]
  3. P. Ango Nze, Geometric and subgeometric rates for markovian processes in the neighbourhood of linearity. C. R. Acad. Sci. Paris 326 (1998) 371-376. [Google Scholar]
  4. Y. Baraud, Model selection for regression on a fixed design. Probab. Theory Related Fields 117 (2000) 467-493. [CrossRef] [MathSciNet] [Google Scholar]
  5. Y. Baraud, Model selection for regression on a random design, Preprint 01-10. DMA, École Normale Supérieure (2001). [Google Scholar]
  6. Y. Baraud, F. Comte and G. Viennet, Adaptive estimation in autoregression or β-mixing regression via model selection. Ann. Statist. (to appear). [Google Scholar]
  7. A. Barron, L. Birgé and P. Massart, Risks bounds for model selection via penalization. Probab. Theory Related Fields 113 (1999) 301-413. [Google Scholar]
  8. L. Birgé and P. Massart, An adaptive compression algorithm in Besov spaces. Constr. Approx. 16 (2000) 1-36. [CrossRef] [MathSciNet] [Google Scholar]
  9. L. Birgé and Y. Rozenholc, How many bins must be put in a regular histogram. Working paper (2001). [Google Scholar]
  10. 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]
  11. I. Daubechies, Ten lectures on wavelets. SIAM: Philadelphia (1992). [Google Scholar]
  12. R.A. Devore and C.G. Lorentz, Constructive Approximation. Springer-Verlag (1993). [Google Scholar]
  13. D.L. Donoho and I.M. Johnstone, Minimax estimation via wavelet shrinkage. Ann. Statist. 26 (1998) 879-921. [Google Scholar]
  14. P. Doukhan, Mixing properties and examples. Springer-Verlag (1994). [Google Scholar]
  15. M. Duflo, Random Iterative Models. Springer, Berlin, New-York (1997). [Google Scholar]
  16. M. Hoffmann, On nonparametric estimation in nonlinear AR(1)-models. Statist. Probab. Lett. 44 (1999) 29-45. [CrossRef] [MathSciNet] [Google Scholar]
  17. I.A. Ibragimov, On the spectrum of stationary Gaussian sequences satisfying the strong mixing condition I: Necessary conditions. Theory Probab. Appl. 10 (1965) 85-106. [CrossRef] [Google Scholar]
  18. M. Kohler, On optimal rates of convergence for nonparametric regression with random design, Working Paper. Stuttgart University (1997). [Google Scholar]
  19. A.R. Kolmogorov and Y.A. Rozanov, On the strong mixing conditions for stationary Gaussian sequences. Theory Probab. Appl. 5 (1960) 204-207. [CrossRef] [Google Scholar]
  20. K.C. Li, Asymptotic optimality for Cp, Cl cross-validation and generalized cross-validation: Discrete index set. Ann. Statist. 15 (1987) 958-975. [CrossRef] [MathSciNet] [Google Scholar]
  21. G.G. Lorentz, M. von Golitschek and Y. Makokov, Constructive Approximation, Advanced Problems. Springer, Berlin (1996). [Google Scholar]
  22. C.L. Mallows, Some comments on Cp. Technometrics 15 (1973) 661-675. [CrossRef] [Google Scholar]
  23. A. Meyer, Quelques inégalités sur les martingales d'après Dubins et Freedman, Séminaire de Probabilités de l'Université de Strasbourg. Vols. 68/69 (1969) 162-169. [Google Scholar]
  24. D.S. Modha and E. Masry, Minimum complexity regression estimation with weakly dependent observations. IEEE Trans. Inform. Theory 42 (1996) 2133-2145. [CrossRef] [MathSciNet] [Google Scholar]
  25. D.S. Modha and E. Masry, Memory-universal prediction of stationary random processes. IEEE Trans. Inform. Theory 44 (1998) 117-133. [CrossRef] [MathSciNet] [Google Scholar]
  26. M. Neumann and J.-P. Kreiss, Regression-type inference in nonparametric autoregression. Ann. Statist. 26 (1998) 1570-1613. [CrossRef] [MathSciNet] [Google Scholar]
  27. B.T. Polyak and A. Tsybakov, A family of asymptotically optimal methods for choosing the order of a projective regression estimate. Theory Probab. Appl. 37 (1992) 471-481. [CrossRef] [MathSciNet] [Google Scholar]
  28. R. Shibata, Selection of the order of an autoregressive model by Akaike's information criterion. Biometrika 63 (1976) 117-126. [CrossRef] [MathSciNet] [Google Scholar]
  29. R. Shibata, An optimal selection of regression variables. Biometrika 68 (1981) 45-54. [CrossRef] [MathSciNet] [Google Scholar]
  30. S. Van de Geer, Exponential inequalities for martingales, with application to maximum likelihood estimation for counting processes. Ann. Statist. 23 (1995) 1779-1801. [CrossRef] [MathSciNet] [Google Scholar]
  31. V.A. Volonskii and Y.A. Rozanov, Some limit theorems for random functions. I. Theory Probab. Appl. 4 (1959) 179-197. [Google Scholar]

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