Partial heteroscedastic deconvolution estimation in nonparametric regression
Statistics Theory
2026-01-29 v1 Statistics Theory
Abstract
In this paper, we consider a partial deconvolution kernel estimator for nonparametric regression when some covariates are measured with error while others are observed without error. We focus on a general and realistic setting in which the measurement errors are heteroscedastic. We propose a kernel-based estimator of the regression function in this framework and show that it achieves the optimal convergence rate under suitable regularity conditions. The finite-sample performance of the proposed estimator is illustrated through simulation studies.
Cite
@article{arxiv.2601.20341,
title = {Partial heteroscedastic deconvolution estimation in nonparametric regression},
author = {Baba Thiam},
journal= {arXiv preprint arXiv:2601.20341},
year = {2026}
}