Deconvolution for an atomic distribution: rates of convergence

TL;DR

This paper develops nonparametric estimators for the density of an unobservable component and a Bernoulli probability in a deconvolution model, achieving optimal convergence rates.

Contribution

It introduces Fourier-based kernel estimators for density and probability, proving their rate-optimality in various settings.

Findings

Estimators achieve the best possible convergence rates.

Derived explicit convergence rates over functional classes.

Established lower bounds confirming estimator optimality.

Abstract

Let $X_1,..., X_n$ be i.i.d.\ copies of a random variable $X=Y+Z,$ where $ X_i=Y_i+Z_i,$ and $Y_i$ and $Z_i$ are independent and have the same distribution as $Y$ and $Z,$ respectively. Assume that the random variables $Y_i$'s are unobservable and that $Y=AV,$ where $A$ and $V$ are independent, $A$ has a Bernoulli distribution with probability of success equal to $1-p$ and $V$ has a distribution function $F$ with density $f.$ Let the random variable $Z$ have a known distribution with density $k.$ Based on a sample $X_1,...,X_n,$ we consider the problem of nonparametric estimation of the density $f$ and the probability $p.$ Our estimators of $f$ and $p$ are constructed via Fourier inversion and kernel smoothing. We derive their convergence rates over suitable functional classes. By establishing in a number of cases the lower bounds for estimation of $f$ and $p$ we show that our estimators are rate-optimal in these cases.