Nonparametric Drift Estimation for Multidimensional Stochastic Differential Equations under Censoring

Abstract

In many applications, a diffusion trajectory is observed without distortion only while it remains inside a time-varying region; outside this region, only its nearest boundary point and an indicator of visibility are recorded. We formalize this setting through a censoring scheme for multidimensional stochastic differential equations, in which the latent state is observed via its Euclidean projection onto a random, time-dependent closed convex set. Classical arguments handle the one-dimensional case. In higher dimensions, however, the projection generates additional finite-variation terms, and it is not immediate that the local information required for drift estimation is preserved under censoring. We show that it is. Under a suitable regularity condition on the projection map, allowing the censoring region to be nonsmooth, the observed process is a continuous semimartingale and admits a generalized It{\^o} decomposition, whose finite-variation correction term assigns no mass to visible times. Consequently, stochastic integrals over visible times coincide exactly for the observed and latent processes. Building on this identity, we construct a Nadaraya-Watson-type drift estimator with a data-driven bandwidth selection rule and establish oracle inequalities. We also derive anisotropic minimax lower bounds for both the full-observation and censored experiments.