EDP Sciences logo
Subscriber Authentication Point
Search
Menu
All issues Volume 17 (2013) ESAIM: PS, 17 (2013) 328-358 References
Free Access
Issue
ESAIM: PS
Volume 17, 2013
Page(s) 328 - 358
DOI https://doi.org/10.1051/ps/2011165
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]

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.