Adaptive exact recovery in sparse nonparametric models

Abstrakt

We observe an unknown function of d variables f(t), t ∈ [0, 1]^d, in the Gaussian white noise model of intensity ε > 0. We assume that the function f is regular and that it is a sum of k-variate functions, where k varies from 1 to s (1 ≤ s ≤ d). These functions are unknown to us and only a few of them are nonzero. In this article, we address the problem of identifying the nonzero components of f in the case when d = d_ε → ∞ as ε → 0 and s is either fixed or s = s_ε → ∞, s = o(d) as ε → ∞. This may be viewed as a variable selection problem. We derive the conditions when exact variable selection in the model at hand is possible and provide a selection procedure that achieves this type of selection. The procedure is adaptive to a degree of model sparsity described by the sparsity parameter β ∈ (0, 1). We also derive conditions that make the exact variable selection impossible. Our results augment previous work in this area.