A uniform central limit theorem and efficiency for deconvolution estimators

TL;DR

This paper establishes a uniform central limit theorem for kernel-based linear functional estimators in deconvolution problems, demonstrating their efficiency and broad applicability under certain smoothness conditions.

Contribution

It introduces a uniform CLT with $\

Findings

Establishes a $\

shows estimators are semiparametrically efficient

applies to distribution function estimation

Abstract

We estimate linear functionals in the classical deconvolution problem by kernel estimators. We obtain a uniform central limit theorem with $\sqrt{n}$-rate on the assumption that the smoothness of the functionals is larger than the ill-posedness of the problem, which is given by the polynomial decay rate of the characteristic function of the error. The limit distribution is a generalized Brownian bridge with a covariance structure that depends on the characteristic function of the error and on the functionals. The proposed estimators are optimal in the sense of semiparametric efficiency. The class of linear functionals is wide enough to incorporate the estimation of distribution functions. The proofs are based on smoothed empirical processes and mapping properties of the deconvolution operator.