Deconvolution for an atomic distribution

TL;DR

This paper develops a kernel deconvolution method to estimate the density and zero probability of a mixture distribution from noisy observations, providing asymptotic normality results.

Contribution

It introduces a novel kernel deconvolution estimator for a mixture model with Bernoulli and continuous components, along with asymptotic analysis.

Findings

Estimator behaves like classical deconvolution estimators

Asymptotic normality at fixed points established

Consistent estimator for the zero probability p

Abstract

Let $X_1,...,X_n$ be i.i.d. observations, where $X_i=Y_i+\sigma Z_i$ and $Y_i$ and $Z_i$ are independent. Assume that unobservable $Y$'s are distributed as a random variable $UV,$ where $U$ and $V$ are independent, $U$ has a Bernoulli distribution with probability of zero equal to $p$ and $V$ has a distribution function $F$ with density $f.$ Furthermore, let the random variables $Z_i$ have the standard normal distribution and let $\sigma>0.$ Based on a sample $X_1,..., X_n,$ we consider the problem of estimation of the density $f$ and the probability $p.$ We propose a kernel type deconvolution estimator for $f$ and derive its asymptotic normality at a fixed point. A consistent estimator for $p$ is given as well. Our results demonstrate that our estimator behaves very much like the kernel type deconvolution estimator in the classical deconvolution problem.