EDP Sciences logo
Subscriber Authentication Point
Search
Menu
All issues Volume 17 (2013) ESAIM: PS, 17 (2013) 33-69 References
Free Access
Issue
ESAIM: PS
Volume 17, 2013
Page(s) 33 - 69
DOI https://doi.org/10.1051/ps/2011102
Published online 06 December 2012
  1. S. Arlot and P. Massart, Data-driven calibration of penalties for least-squares regression. J. Mach. Learn. Res. 10 (2009) 245–279. [Google Scholar]
  2. 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]
  3. A. Beskos, O. Papaspiliopoulos and G.O. Roberts, Retrospective exact simulation of diffusion sample paths with applications. Bernoulli 12 (2006) 1077–1098. [CrossRef] [MathSciNet] [Google Scholar]
  4. D. Bosq, Parametric rates of nonparametric estimators and predictors for continuous time processes. Ann. Stat. 25 (1997) 982–1000. [CrossRef] [Google Scholar]
  5. F. Comte and F. Merlevède, Adaptive estimation of the stationary density of discrete and continuous time mixing processes. ESAIM : PS 6 (2002) 211–238 (electronic). New directions in time series analysis. Luminy (2001). [CrossRef] [EDP Sciences] [Google Scholar]
  6. F. Comte and F. Merlevède, Super optimal rates for nonparametric density estimation via projection estimators. Stoc. Proc. Appl. 115 (2005) 797–826. [CrossRef] [Google Scholar]
  7. F. Comte, Y. Rozenholc and M.L. Taupin, Penalized contrast estimator for adaptive density deconvolution. Can. J. Stat. 34 (2006) 431–452. [CrossRef] [Google Scholar]
  8. F. Comte, V. Genon-Catalot and Y. Rozenholc, Penalized nonparametric mean square estimation of the coefficients of diffusion processes. Bernoulli 13 (2007) 514–543. [CrossRef] [MathSciNet] [Google Scholar]
  9. A.S. Dalalyan and Y.A. Kutoyants, Asymptotically efficient estimation of the derivative of the invariant density. Stat. Inference Stoch. Process. 6 (2003) 89–107. [CrossRef] [MathSciNet] [Google Scholar]
  10. R.A. DeVore and G.G. Lorentz, Constructive approximation. Grundlehren der Mathematischen Wissenschaften [Fundamental Principles of Mathematical Sciences] 303 (1993). [Google Scholar]
  11. A. Gloter, Discrete sampling of an integrated diffusion process and parameter estimation of the diffusion coefficient. ESAIM : PS 4 (2000) 205–227. [Google Scholar]
  12. E. Gobet, M. Hoffmann and M. Reiß, Nonparametric estimation of scalar diffusions based on low frequency data. Ann. Stat. 32 (2004) 2223–2253. [Google Scholar]
  13. N. Hosseinioun, H. Doosti and H.A. Niroumand, Wavelet-based estimators of the integrated squared density derivatives for mixing sequences. Pakistan J. Stat. 25 (2009) 341–350. [Google Scholar]
  14. C. Lacour, Estimation non paramétrique adaptative pour les chaînes de Markov et les chaînes de Markov cachées. Ph.D. thesis, Université Paris Descartes (2007). [Google Scholar]
  15. C. Lacour, Nonparametric estimation of the stationary density and the transition density of a Markov chain. Stoc. Proc. Appl. 118 (2008) 232–260. [Google Scholar]
  16. F. Leblanc, Density estimation for a class of continuous time processes. Math. Methods Stat. 6 (1997) 171–199. [Google Scholar]
  17. M. Lerasle, Adaptive density estimation of stationary β-mixing and τ-mixing processes. Math. Methods Stat. 18 (2009) 59–83. [CrossRef] [Google Scholar]
  18. M. Lerasle, Optimal model selection for stationary data under various mixing conditions (2010). [Google Scholar]
  19. E. Masry, Probability density estimation from dependent observations using wavelets orthonormal bases. Stat. Probab. Lett. 21 (1994) 181–194. Available on : http://dx.doi.org/10.1016/0167-7152(94)90114-7. [CrossRef] [Google Scholar]
  20. Y. Meyer, Ondelettes et opérateurs I. Actualités Mathématiques [Current Mathematical Topics]. Hermann, Paris, Ondelettes [Wavelets] (1990). [Google Scholar]
  21. E. Pardoux and A.Y. Veretennikov, On the Poisson equation and diffusion approximation I. Ann. Probab. 29 (2001) 1061–1085. [CrossRef] [MathSciNet] [Google Scholar]
  22. B.L.S.P. Rao, Nonparametric estimation of the derivatives of a density by the method of wavelets. Bull. Inform. Cybernet. 28 (1996) 91–100. [MathSciNet] [Google Scholar]
  23. E. Schmisser, Non-parametric drift estimation for diffusions from noisy data. Stat. Decis. 28 (2011) 119–150. [CrossRef] [Google Scholar]
  24. G. Viennet, Inequalities for absolutely regular sequences : application to density estimation. Probab. Theory Relat. Fields (1997). [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.