FASTER UNIFORM CONVERGENCE RATES FOR DECONVOLUTION ESTIMATORS FROM REPEATED MEASUREMENTS
证明在相同假设下，Kurisu和Otsu（2022）提出的非参数反卷积估计量可达到更快的一致收敛速度，并基于Kotlarski恒等式的变体提出一类新估计量，在某些情况下收敛速度更快。
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.