Near-optimal recovery of linear and N-convex functions on unions of convex sets

Abstract

Abstract In this paper we build provably near-optimal, in the minimax sense, estimates of linear forms and, more generally, ‘$N$-convex functionals’ (an example being the maximum of several fractional-linear functions) of unknown ‘signal’ from indirect noisy observations, the signal assumed to belong to the union of finitely many given convex compact sets. Our main assumption is that the observation scheme in question is good in the sense of Goldenshluger et al. (2015, Electron. J. Stat., 9, 1645–1712), the simplest example being the Gaussian scheme, where the observation is the sum of linear image of the signal and the standard Gaussian noise. The proposed estimates, same as upper bounds on their worst-case risks, stem from solutions to explicit convex optimization problems, making the estimates ‘computation-friendly’.