Free Access
Volume 20, 2016
Page(s) 143 - 153
Published online 14 July 2016
  1. S.A. van de Geer, Applications of Empirical Process Theory. Vol. 6 of Cambridge Series in Statistical and Probabilistic Mathematics. Cambridge University Press, Cambridge (2000). [Google Scholar]
  2. V. Genon-Catalot, C. Laredo and D. Picard, Nonparametric estimation of the diffusion coefficient by wavelets methods. Scand. J. Statist. 19 (1992) 317–335. [MathSciNet] [Google Scholar]
  3. S. Ghosal, J.K. Ghosh and A.W. van der Vaart, Convergence rates of posterior distributions. Ann. Statist. 28 (2000) 500–531. [CrossRef] [MathSciNet] [Google Scholar]
  4. S. Ghosal and A.W. van der Vaart, Convergence rates of posterior distributions for non-i.i.d. observations. Ann. Statist. 35 (2007) 192–223. [CrossRef] [MathSciNet] [Google Scholar]
  5. S. Ghosal, J.K. Ghosh and R.V. Ramamoorthi, Non-informative priors via sieves and packing numbers. Advances in Statistical Decision Theory and Applications, Stat. Ind. Technol. Birkhäuser Boston, Boston, MA (1997) 119–132. [Google Scholar]
  6. S. Ghosal, J.K. Ghosh and R.V. Ramamoorthi, Consistency issues in Bayesian nonparametrics. Asymptotics, Nonparametrics, and Time Series. Vol. 158 of Statist. Textbooks Monogr. Dekker, New York (1999) 639–667. [Google Scholar]
  7. S. Gugushvili and P. Spreij, Non-parametric Bayesian estimation of a dispersion coefficient of the stochastic differential equation. ESAIM: PS 18 (2014) 332–341. [CrossRef] [EDP Sciences] [Google Scholar]
  8. M. Hoffmann, Minimax estimation of the diffusion coefficient through irregular samplings. Statist. Probab. Lett. 32 (1997) 11–24. [CrossRef] [MathSciNet] [Google Scholar]
  9. I. Karatzas and S.E. Shreve, Brownian Motion and Stochastic Calculus. Vol. 113 of Graduate Texts in Mathematics. Springer-Verlag, New York (1988). [Google Scholar]
  10. B. Kleijn and A.W. van der Vaart, Misspecification in infinite-dimensional Bayesian statistics, Ann. Statist. 34 (2006) 837–877. [CrossRef] [MathSciNet] [Google Scholar]
  11. R. Nickl and J. Söhl, Nonparametric Bayesian posterior contraction rates for discretely observed scalar diffusions. Preprint arXiv:1510.05526 [math.ST] (2015). [Google Scholar]
  12. C.E. Rasmussen and C.K.I. Williams, Gaussian Processes for Machine Learning. Adaptive Computation and Machine Learning. MIT Press, Cambridge, MA (2006). [Google Scholar]
  13. X. Shen and L. Wasserman, Rates of convergence of posterior distributions. Ann. Statist. 29 (2001) 687–714. [CrossRef] [MathSciNet] [Google Scholar]
  14. P. Soulier, Nonparametric estimation of the diffusion coefficient of a diffusion process. Stochastic Anal. Appl. 16 (1998) 185–200. [CrossRef] [MathSciNet] [Google Scholar]
  15. A.B. Tsybakov, Introduction to Nonparametric Estimation. Springer Series in Statistics. Revised and extended from the 2004 French original. Translated by Vladimir Zaiats. Springer, New York (2009). [Google Scholar]
  16. A.W. van der Vaart and J.H. van Zanten, Rates of contraction of posterior distributions based on Gaussian process priors. Ann. Statist. 36 (2008) 1435–1463. [CrossRef] [MathSciNet] [Google Scholar]
  17. J. van Waaij and H. van Zanten, Gaussian process methods for one-dimensional diffusions: optimal rates and adaptation. Preprint arXiv:1506.00515 [math.ST] (2015). [Google Scholar]
  18. L. Wasserman, Asymptotic properties of nonparametric Bayesian procedures. In Practical Nonparametric and Semiparametric Bayesian Statistics. Vol. 133 of Lect. Notes Stat. Springer, New York (1998) 293–304. [Google Scholar]
  19. W.H. Wong and X. Shen, Probability inequalities for likelihood ratios and convergence rates of sieve MLEs. Ann. Statist. 23 (1995) 339–362. [CrossRef] [MathSciNet] [Google Scholar]

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