Estimation of the invariant density for discretely observed diffusion processes: impact of the sampling and of the asynchronicity

We aim at estimating in a non-parametric way the density pi of the stationary distribution of a d-dimensional stochastic differen-tial equation (X-t)(t is an element of[0,T]), for d >= 2, from the discrete observations of a finite sample X-t0,..., X-tn with 0 = t(0) < t(1) < middot middot middot < t(n) =: T-n. We propose a kernel density estimator and we study its convergence rates for the pointwise estimation of the invariant density under anisotropic Holder smoothness constraints. First of all, we find some conditions on the discretization step that ensures it is possible to recover the same rates as if the continuous trajectory of the pro-cess was available. As proven in the recent work [Amorino C, Gloter A. Minimax rate of estimation for invariant densities associated to continuous stochastic differential equations over anisotropic Holder classes; 2021. arXiv preprint arXiv:2110.02774], such rates are opti-mal and new in the context of density estimator. Then we deal with the case where such a condition on the discretization step is not satis-fied, which we refer to as the intermediate regime. In this new regime we identify the convergence rate for the estimation of the invariant density over anisotropic Holder classes, which is the same conver-gence rate as for the estimation of a probability density belonging to an anisotropic Holder class, associated to n iid random variables X-1,...,X-n. After that we focus on the asynchronous case, in which each component can be observed at different time points. Even if the asynchronicity of the observations complexifies the computation of the variance of the estimator, we are able to find conditions ensuring that this variance is comparable to the one of the continuous case. We also exhibit that the non-synchronicity of the data introduces additional bias terms in the study of the estimator.