ESTIMATION OF THE INVARIANT MEASURE OF A MULTIDIMENSIONAL DIFFUSION FROM NOISY OBSERVATIONS

Raphaël Maillet (1) , Grégoire Szymanski (2)

1 CEREMADE - CEntre de REcherches en MAthématiques de la DEcision
2 CMAP - Centre de Mathématiques Appliquées - Ecole Polytechnique

We introduce a new approach for estimating the invariant density of a multidimensional diffusion when dealing with high-frequency observations blurred by independent noises. We consider the intermediate regime, where observations occur at discrete time instances $k\Delta_n$ for $k=0,\dots,n$, under the conditions $\Delta_n\to 0$ and $n\Delta_n\to\infty$. Our methodology involves the construction of a kernel density estimator that uses a pre-averaging technique to proficiently remove noise from the data while preserving the analytical characteristics of the underlying signal and its asymptotic properties. The rate of convergence of our estimator depends on both the anisotropic regularity of the density and the intensity of the noise. We establish conditions on the intensity of the noise that ensure the recovery of convergence rates similar to those achievable without any noise. Furthermore, we prove a Bernstein concentration inequality for our estimator, from which we derive an adaptive procedure for the kernel bandwidth selection.

Mots clés: Non-parametric estimation; high-frequency asymptotics; noisy observation; pre-averaging; ergodic diffusion; anisotropic density estimation; concentration inequality

Domaines: Statistiques [math.ST]

Raphaël Maillet, Grégoire Szymanski. ESTIMATION OF THE INVARIANT MEASURE OF A MULTIDIMENSIONAL DIFFUSION FROM NOISY OBSERVATIONS. 2024. ⟨hal-04576753⟩