Deconvolution problem of cumulative distribution function with heteroscedastic errors

We study the nonparametric deconvolution problem of cumulative distribution function when measurement errors are heteroscedastic and have known distributions. Using a Fourier-type deconvolution method, we propose an estimator for the target function that depends only on a regularization parameter. O...

Full description

Saved in:
Bibliographic Details
Published in:Journal of the Korean Statistical Society 2023-06, Vol.52 (2), p.330-360
Main Authors: Thuy, Le Thi Hong, Phuong, Cao Xuan
Format: Article
Language:English
Subjects:
Applied Statistics
Bayesian Inference
Mathematics and Statistics
Research Article
Statistical Theory and Methods
Statistics
Statistics and Computing/Statistics Programs
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:We study the nonparametric deconvolution problem of cumulative distribution function when measurement errors are heteroscedastic and have known distributions. Using a Fourier-type deconvolution method, we propose an estimator for the target function that depends only on a regularization parameter. Our estimator achieves minimax optimal convergence rates when the errors are all either ordinary smooth or supersmooth. A simulation study is also conducted to illustrate the effectiveness of the proposed estimator.
ISSN:1226-3192
2005-2863
DOI:10.1007/s42952-023-00203-w