Minimax theory for a class of non-linear statistical inverse problems

Abstract

We study a class of statistical inverse problems with non-linear pointwise operators motivated by concrete statistical applications. A two-step procedure is proposed, where the first step smoothes the data and inverts the non-linearity. This reduces the initial non-linear problem to a linear inverse problem with deterministic noise, which is then solved in a second step. The noise reduction step is based on wavelet thresholding and is shown to be minimax optimal (up to logarithmic factors) in a pointwise function-dependent sense. Our analysis is based on a modified notion of Hölder smoothness scales that are natural in this setting.

37 pages

References in corpus (3)

• Nonparametric regression in exponential families
• Asymptotic equivalence of functional linear regression and a white noise inverse problem
• Adaptation to lowest density regions with application to support recovery

Cited by in corpus (3)

• Convergence analysis of Tikhonov regularization for non-linear statistical inverse learning problems
• A regularity class for the roots of non-negative functions
• Asymptotic nonequivalence of density estimation and Gaussian white noise for small densities