Nonparametric Bayesian posterior contraction rates for discretely observed scalar diffusions

Abstract

We consider nonparametric Bayesian inference in a reflected diffusion model $d{X}_t=b(X_t)dt+\sigma (X_t)d{W}_t,$ with discretely sampled observations $X_0,X_{\Delta },\dots ,X_{n\Delta }$. We analyse the nonlinear inverse problem corresponding to the `low frequency sampling' regime where $\Delta >0$ is fixed and $n\to \infty$. A general theorem is proved that gives conditions for prior distributions $\Pi$ on the diffusion coefficient $\sigma$ and the drift function $b$ that ensure minimax optimal contraction rates of the posterior distribution over Hölder-Sobolev smoothness classes. These conditions are verified for natural examples of nonparametric random wavelet series priors. For the proofs we derive new concentration inequalities for empirical processes arising from discretely observed diffusions that are of independent interest.

44 pages

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• Rates of contraction of posterior distributions based on Gaussian process priors
• Convergence rates of posterior distributions for noniid observations
• Renewal theory and computable convergence rates for geometrically ergodic Markov chains
• Nonparametric Bayesian posterior contraction rates for discretely observed scalar diffusions
• Rates of contraction for posterior distributions in $\bolds{L^r}$-metrics, $\bolds{1\le r\le\infty}$
• Consistent nonparametric Bayesian inference for discretely observed scalar diffusions

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