Density Deconvolution with Nonstandard Error Distributions: Rates of Convergence and Adaptive Estimation

• Activity Number: 444 - Recent Advances in Statistical Methodology for Big Data
• Type: Contributed
• Date/Time: Thursday, August 12, 2021 : 4:00 PM to 5:50 PM
• Sponsor: IMS
• Author(s): Alexander Goldenshluger and Taeho Kim*
• Companies: University of Haifa and University of Haifa
• Keywords: Density Deconvolution; Minimax Risk; Characteristic Function; Laplace Transform; Non-standard Measurement Error; Zero Multiplicity

Abstract

It is a standard assumption in the density deconvolution problem that the characteristic function of the measurement error distribution is non-zero on the real line. While this condition is assumed in the majority of existing works on the topic, there are many problem instances of interest where it is violated. In this paper, we focus on non-standard settings where the characteristic function of the measurement errors has zeros, and study how zeros multiplicity affects the estimation accuracy. For a prototypical problem of this type, we demonstrate that the best achievable estimation accuracy is determined by the multiplicity of zeros, the rate of decay of the error characteristic function, as well as by the smoothness and the tail behavior of the estimated density. We derive lower bounds on the minimax risk and develop optimal in the minimax sense estimators. In addition, we consider the problem of adaptive estimation and propose a data-driven estimator that automatically adapts to unknown smoothness and tail behavior of the density to be estimated.