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Consistent non-parametric Bayesian estimation for atime-inhomogeneous Brownian motion

Published online by Cambridge University Press:  03 October 2014

Shota Gugushvili
Affiliation:
Mathematical Institute, Leiden University, P.O. Box 9512, 2300 RA Leiden, The Netherlands. [email protected]
Peter Spreij
Affiliation:
Korteweg-de Vries Institute for Mathematics, University of Amsterdam, P.O. Box 94248, 1090 GE Amsterdam, The Netherlands; [email protected]
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Abstract

We establish posterior consistency for non-parametric Bayesian estimation of thedispersion coefficient of a time-inhomogeneous Brownian motion.

Type
Research Article
Copyright
© EDP Sciences, SMAI 2014

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References

Barron, A., Schervish, M.J. and Wasserman, L., The consistency of posterior distributions in nonparametric problems. Ann. Statist. 27 (1999) 536561. Google Scholar
Choudhuri, N., Ghosal, S. and Roy, A., Bayesian estimation of the spectral density of a time series. J. Amer. Statist. Assoc. 99 (2004) 10501059. Google Scholar
Diaconis, P. and Freedman, D., On the consistency of Bayes estimates. With a discussion and a rejoinder by the authors. Ann. Statist. 14 (1986) 167. Google Scholar
Genon-Catalot, V. and Jacod, J., On the estimation of the diffusion coefficient for multi-dimensional diffusion processes. Ann. Inst. Henri Poincaré Probab. Statist. 29 (1993) 119151. Google Scholar
Genon-Catalot, V., Laredo, C. and Picard, D., Nonparametric estimation of the diffusion coefficient by wavelets methods. Scand. J. Statist. 19 (1992) 317335. Google Scholar
S. Ghosal, J.K. Ghosh and R.V. Ramamoorthi, Consistency issues in Bayesian nonparametrics. Asymptotics, Nonparametrics, and Time Series. Vol. 158 of Textbooks Monogr. Dekker, New York (1999) 639–667.
Ghosal, S. and Tang, Y., Bayesian consistency for Markov processes. Sankhyā 68 (2006) 227239. Google Scholar
S. Gugushvili and P. Spreij, Non-parametric Bayesian drift estimation for stochastic differential equations (2012). Preprint arXiv:1206.4981 [math.ST].
Hoffmann, M., Minimax estimation of the diffusion coefficient through irregular samplings. Statist. Probab. Lett. 32 (1997) 1124. Google Scholar
I.A. Ibragimov and R.Z. Has′minskiĭ, Asimptoticheskaya teoriya otsenivaniya [Asymptotic Theory of Estimation] (Russian). Nauka, Moscow (1979).
van der Meulen, F., Schauer, M. and van Zanten, H., Reversible jump MCMC for nonparametric drift estimation for diffusion processes. Comput. Statist. Data Anal. 71 (2014) 615632. Available on http://dx.doi.org/10.1016/j.csda.2013.03.002. Google Scholar
van der Meulen, F.H., van der Vaart, A.W. and van Zanten, J.H., Convergence rates of posterior distributions for Brownian semimartingale models. Bernoulli 12 (2006) 863888. Google Scholar
van der Meulen, F. and van Zanten, H., Consistent nonparametric Bayesian estimation for discretely observed scalar diffusions. Bernoulli 19 (2013) 4463. Google Scholar
Panzar, L. and van Zanten, H., Nonparametric Bayesian inference for ergodic diffusions. J. Statist. Plann. Inference 139 (2009) 41934199. Google Scholar
Papaspiliopoulos, O., Pokern, Y., Roberts, G.O. and Stuart, A.M., Nonparametric estimation of diffusions: a differential equations approach. Biometrika 99 (2012) 511531. Google Scholar
G.A. Pavliotis, Y. Pokern and A.M. Stuart, Parameter estimation for multiscale diffusions: an overview. Statistical Methods for Stochastic Differential Equations. Vol. 124 of Monogr. Statist. Appl. Probab. CRC Press, Boca Raton, FL (2012) 429–472.
Pokern, Y., Stuart, A.M. and van Zanten, J.H.. Posterior consistency via precision operators for nonparametric drift estimation in SDEs. Stoch. Process. Appl. 123 (2013) 603628. Google Scholar
Schwartz, L., On Bayes procedures. Z. Wahrscheinlichkeitstheorie und Verw. Gebiete 4 (1965) 1026. Google Scholar
Soulier, P., Nonparametric estimation of the diffusion coefficient of a diffusion process. Stochastic Anal. Appl. 16 (1998) 185200. Google Scholar
A.W. van der Vaart, Asymptotic Statistics. Vol. 3 of Cambr. Ser. Stat. Probab. Math. Cambridge University Press, Cambridge (1998).
van der Vaart, A.W. and van Zanten, J.H., Rates of contraction of posterior distributions based on Gaussian process priors. Ann. Statist. 36 (2008a) 14351463. Google Scholar
A.W. van der Vaart and J.H. van Zanten, Reproducing kernel Hilbert spaces of Gaussian priors. Pushing the Limits of Contemporary Statistics: Contributions in Honor of Jayanta K. Ghosh. Vol. 3 of Inst. Math. Stat. Collect. Inst. Math. Statist., Beachwood, OH (2008) 200–222.
Walker, S., On sufficient conditions for Bayesian consistency. Biometrika 90 (2003) 482488. Google Scholar
Walker, S., New approaches to Bayesian consistency. Ann. Statist. 32 (2004) 20282043. Google Scholar
L. Wasserman, Asymptotic properties of nonparametric Bayesian procedures. Practical Nonparametric and Semiparametric Bayesian Statistics. Vol. 133 of Lect. Notes Statist. Springer, New York (1998) 293–304.
H. van Zanten, Nonparametric Bayesian methods for one-dimensional diffusion models. Math. Biosci. (2013). Available on http://dx.doi.org/10.1016/j.mbs.2013.03.008. CrossRef