Bernoulli

Consistent nonparametric Bayesian inference for discretely observed scalar diffusions

Frank van der Meulen and Harry van Zanten

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Abstract

We study Bayes procedures for the problem of nonparametric drift estimation for one-dimensional, ergodic diffusion models from discrete-time, low-frequency data. We give conditions for posterior consistency and verify these conditions for concrete priors, including priors based on wavelet expansions.

Article information

Source
Bernoulli, Volume 19, Number 1 (2013), 44-63.

Dates
First available in Project Euclid: 18 January 2013

Permanent link to this document
https://projecteuclid.org/euclid.bj/1358531740

Digital Object Identifier
doi:10.3150/11-BEJ385

Mathematical Reviews number (MathSciNet)
MR3019485

Zentralblatt MATH identifier
1259.62070

Keywords
Bayesian nonparametrics drift function posterior consistency posterior distribution stochastic differential equations wavelets

Citation

van der Meulen, Frank; van Zanten, Harry. Consistent nonparametric Bayesian inference for discretely observed scalar diffusions. Bernoulli 19 (2013), no. 1, 44--63. doi:10.3150/11-BEJ385. https://projecteuclid.org/euclid.bj/1358531740


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