Free Access
Volume 11, February 2007
Special Issue: "Stochastic analysis and mathematical finance" in honor of Nicole El Karoui's 60th birthday
Page(s) 344 - 364
Published online 17 August 2007
  1. A. Antoniadis, G. Gregoire and P. Vial, Random design wavelet curve smoothing. Statist. Probab. Lett. 35 (1997) 225–232. [CrossRef] [MathSciNet] [Google Scholar]
  2. Y. Baraud, Model selection for regression on a random design. ESAIM Probab. Statist. 6 (2002) 127–146 (electronic). [Google Scholar]
  3. N.H. Bingham, C.M. Goldie and J.L. Teugels, Regular Variation. Encyclopedia of Mathematics and its Applications, Cambridge University Press (1989). [Google Scholar]
  4. L. Brown and T. Cai, Wavelet shrinkage for nonequispaced samples. Ann. Statist. 26 (1998) 1783–1799. [CrossRef] [MathSciNet] [Google Scholar]
  5. L.D. Brown and M.G. Low, A constrained risk inequality with applications to nonparametric functional estimations. Ann. Statist. 24 (1996) 2524–2535. [CrossRef] [MathSciNet] [Google Scholar]
  6. T.T. Cai, M. Low and L.H. Zhao, Tradeoffs between global and local risks in nonparametric function estimation. Tech. rep., Wharton, University of Pennsylvania, (2004). [Google Scholar]
  7. V. Delouille, J. Simoens and R. Von Sachs, Smooth design-adapted wavelets for nonparametric stochastic regression. J. Amer. Statist. Soc. 99 (2004) 643–658. [Google Scholar]
  8. J. Fan and I. Gijbels, Data-driven bandwidth selection in local polynomial fitting: variable bandwidth and spatial adaptation. J. Roy. Statist. Soc. Ser. B. Methodological 57 (1995) 371–394. [Google Scholar]
  9. J. Fan and I. Gijbels, Local polynomial modelling and its applications. Monographs on Statistics and Applied Probability, Chapman & Hall, London (1996). [Google Scholar]
  10. S. Gaïffas, Convergence rates for pointwise curve estimation with a degenerate design. Mathematical Methods of Statistics 1 (2005) 1–27. Available at [Google Scholar]
  11. A. Goldenshluger and A. Nemirovski, On spatially adaptive estimation of nonparametric regression. Math. Methods Statist. 6 (1997) 135–170. [Google Scholar]
  12. G. Kerkyacharian and D. Picard, Regression in random design and warped wavelets. Bernoulli, 10 (2004) 1053–1105. [Google Scholar]
  13. O.V. Lepski, Asymptotically minimax adaptive estimation i: Upper bounds, optimally adaptive estimates. Theory Probab. Applic. 36 (1988) 682–697. [Google Scholar]
  14. O.V. Lepski, On a problem of adaptive estimation in Gaussian white noise. Theory Probab. Appl., 35 (1990) 454–466. [Google Scholar]
  15. O.V. Lepski, E. Mammen and V.G. Spokoiny, Optimal spatial adaptation to inhomogeneous smoothness: an approach based on kernel estimates with variable bandwidth selectors. Ann. Statist. 25 (1997) 929–947. [Google Scholar]
  16. O.V. Lepski and V.G. Spokoiny, Optimal pointwise adaptive methods in nonparametric estimation. Ann. Statist. 25 (1997) 2512–2546. [Google Scholar]
  17. V. Maxim, Restauration de signaux bruités sur des plans d'experience aléatoires. Ph.D. thesis, Université Joseph Fourier, Grenoble 1 (2003). [Google Scholar]
  18. V.G. Spokoiny, Estimation of a function with discontinuities via local polynomial fit with an adaptive window choice. Ann. Statist. 26 (1998) 1356–1378. [CrossRef] [MathSciNet] [Google Scholar]
  19. C.J. Stone, Optimal rates of convergence for nonparametric estimators. Ann. Statist. 8 (1980) 1348–1360. [CrossRef] [MathSciNet] [Google Scholar]
  20. A. Tsybakov, Introduction à l'estimation non-paramétrique. Springer (2003). [Google Scholar]
  21. M.-Y. Wong and Z. Zheng, Wavelet threshold estimation of a regression function with random design. J. Multivariate Anal. 80 (2002) 256–284. [CrossRef] [MathSciNet] [Google Scholar]

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