Density estimation with heteroscedastic error

TL;DR

This paper introduces a kernel density estimator for heteroscedastic errors in deconvolution problems, establishing its consistency, optimal convergence rates, and practical adaptive procedures, even in extreme cases with unbounded error scales.

Contribution

It develops a novel kernel estimator for heteroscedastic errors, proving its consistency and optimal convergence, and proposes adaptive methods for practical implementation.

Findings

Estimator is consistent under general conditions.

Achieves optimal convergence rates.

Effective even with unbounded error scales.

Abstract

It is common, in deconvolution problems, to assume that the measurement errors are identically distributed. In many real-life applications, however, this condition is not satisfied and the deconvolution estimators developed for homoscedastic errors become inconsistent. In this paper, we introduce a kernel estimator of a density in the case of heteroscedastic contamination. We establish consistency of the estimator and show that it achieves optimal rates of convergence under quite general conditions. We study the limits of application of the procedure in some extreme situations, where we show that, in some cases, our estimator is consistent, even when the scaling parameter of the error is unbounded. We suggest a modified estimator for the problem where the distribution of the errors is unknown, but replicated observations are available. Finally, an adaptive procedure for selecting the smoothing parameter is proposed and its finite-sample properties are investigated on simulated examples.