Nonparametric denoising Signals of Unknown Local Structure, II: Nonparametric Regression Estimation

We consider the problem of recovering of continuous multi-dimensional functions f from the noisy observations over the regular grid m−1Zd, m ∈ N∗. Our focus is at the adaptive estimation in the case when the function can be well recovered using a linear filter, which can depend on the unknown function itself. In the companion paper [26] we have shown in the case when there exists an adapted time-invariant filter, which locally recovers “well” the unknown signal, there is a numerically efficient construction of an adaptive filter which recovers the signals “almost as well”. In the current paper we study the application of the proposed estimation techniques in the non-parametric regression setting. Namely, we propose an adaptive estimation procedure for “locally well-filtered” signals (some typical examples being smooth signals, modulated smooth signals and harmonic functions) and show that the rate of recovery of such signals in the lp-norm on the grid is essentially the same as that rate for regular signals with nonhomogeneous smoothness.