Free Access
Volume 17, 2013
Page(s) 328 - 358
Published online 17 May 2013
  1. A. Antoniadis, G. Grégoire and P. Vial, Random design wavelet curve smoothing. Statist. Probab. Lett. 35 (1997) 225–232. [CrossRef] [MathSciNet] [Google Scholar]
  2. J.Y. Audibert and O. Catoni, Robust linear least squares regression. Ann. Stat. (2011) (to appear), arXiv:1010.0074. [Google Scholar]
  3. J.Y. Audibert and O. Catoni, Robust linear regression through PAC-Bayesian truncation. Preprint, arXiv:1010.0072. [Google Scholar]
  4. Y. Baraud, Model selection for regression on a random design. ESAIM: PS 6 (2002) 127–146. [Google Scholar]
  5. A. Barron, L. Birgé and P. Massart, Risk bounds for model selection via penalization. Probab. Theory Relat. Fields 113 (1999) 301–413. [CrossRef] [MathSciNet] [Google Scholar]
  6. J.P. Baudry, C. Maugis and B. Michel, Slope heuristics: overview and implementation. Stat. Comput. 22-2 (2011) 455–470. [Google Scholar]
  7. L. Birgé, Model selection for Gaussian regression with random design. Bernoulli 10 (2004) 1039–1051. [CrossRef] [MathSciNet] [Google Scholar]
  8. 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]
  9. L. Birgé and P. Massart, Minimal penalties for gaussian model selection. Probab. Theory Relat. Fields 138 (2006) 33–73. [Google Scholar]
  10. E. Brunel and F. Comte, Penalized contrast estimation of density and hazard rate with censored data. Sankhya 67 (2005) 441–475. [Google Scholar]
  11. E. Brunel, F. Comte and A. Guilloux, Nonparametric density estimation in presence of bias and censoring. Test 18 (2009) 166–194. [CrossRef] [MathSciNet] [Google Scholar]
  12. T.T. Cai and L.D. Brown, Wavelet shrinkage for nonequispaced samples. Ann. Stat. 26 (1998) 1783–1799. [Google Scholar]
  13. G. Chagny, Régression: bases déformées et sélection de modèles par pénalisation et méthode de Lepski. Preprint, hal-00519556 v2. [Google Scholar]
  14. F. Comte and Y. Rozenholc, A new algorithm for fixed design regression and denoising. Ann. Inst. Stat. Math. 56 (2004) 449–473. [CrossRef] [Google Scholar]
  15. R.A. DeVore and G. Lorentz, Constructive approximation, Grundlehren der Mathematischen Wissenschaften [Fundamental Principles of Mathematical Sciences], vol. 303. Springer-Verlag, Berlin (1993). [Google Scholar]
  16. D.L. Donoho, I.M. Johnstone, G. Kerkyacharian and D. Picard, Wavelet shrinkage: asymptopia? With discussion and a reply by the authors. J. Roy. Stat. Soc., Ser. B 57 (1995) 301–369. [Google Scholar]
  17. A. Dvoretzky, J. Kiefer and J. Wolfowitz, Asymptotic minimax character of the sample distribution function and of the classical multinomial estimator. Ann. Math. Stat. 27 (1956) 642–669. [CrossRef] [Google Scholar]
  18. S. Efromovich, Nonparametric curve estimation: Methods, theory, and applications. Springer Series in Statistics, Springer-Verlag, New York (1999) xiv+411 [Google Scholar]
  19. J. Fan and I. Gijbels, Variable bandwidth and local linear regression smoothers. Ann. Stat. 20 (1992) 2008–2036. [Google Scholar]
  20. S. Gaïffas, On pointwise adaptive curve estimation based on inhomogeneous data. ESAIM: PS 11 (2007) 344–364. [Google Scholar]
  21. A. Goldenshluger and O. Lepski, Bandwidth selection in kernel density estimation: oracle inequalities and adaptive minimax optimality. Ann. Stat. 39 (2011) 1608–1632. [Google Scholar]
  22. G.K. Golubev and M. Nussbaum, Adaptive spline estimates in a nonparametric regression model. Teor. Veroyatnost. i Primenen. ( Russian) 37 (1992) 554–561; translation in Theor. Probab. Appl. 37 (1992) 521–529. [Google Scholar]
  23. W. Härdle and A. Tsybakov, Local polynomial estimators of the volatility function in nonparametric autoregression. J. Econ. 81 (1997) 223–242. [CrossRef] [Google Scholar]
  24. G. Kerkyacharian and D. Picard, Regression in random design and warped wavelets. Bernoulli 10 (2004) 1053–1105. [CrossRef] [MathSciNet] [Google Scholar]
  25. T. Klein and E. Rio, Concentration around the mean for maxima of empirical processes. Ann. Probab. 33 (2005) 1060–1077. [CrossRef] [MathSciNet] [Google Scholar]
  26. M. Köhler and A. Krzyzak, Nonparametric regression estimation using penalized least squares. IEEE Trans. Inf. Theory 47 (2001) 3054–3058. [CrossRef] [Google Scholar]
  27. C. Lacour, Adaptive estimation of the transition density of a particular hidden Markov chain. J. Multivar. Anal. 99 (2008) 787–814. [CrossRef] [Google Scholar]
  28. E. Nadaraya, On estimating regression. Theory Probab. Appl. 9 (1964) 141–142. [CrossRef] [Google Scholar]
  29. T.-M. Pham Ngoc, Regression in random design and Bayesian warped wavelets estimators. Electron. J. Stat. 3 (2009) 1084–1112. [CrossRef] [Google Scholar]
  30. A.B. Tsybakov, Introduction à l’estimation non-paramétrique, Mathématiques & Applications (Berlin), vol. 41. Springer-Verlag, Berlin (2004). [Google Scholar]
  31. G.S. Watson, Smooth regression analysis. Sankhya A 26 (1964) 359–372. [Google Scholar]
  32. M. Wegkamp, Model selection in nonparametric regression. Ann. Stat. 31 (2003) 252–273. [Google Scholar]

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