by Liang Chen, Minyuan Zhang                                          

Abstract

Recently, Kurisu and Otsu (2022b, Econometric Theory 38(1), 172-193) derived the uniform convergence rates for the nonparametric deconvolution estimators proposed by Li and Vuong (1998, Journal of Multivariate Analysis 65(2), 139-165). This article shows that faster uniform convergence rates can be established for their estimators under the same assumptions. In addition, a new class of deconvolution estimators based on a variant of Kotlarski's identity is also proposed. It is shown that in some cases, these new estimators can have faster uniform convergence rates than the existing estimators.