Debiased inference in error-in-variable problems with non-Gaussian measurement error
Methodology
2025-05-06 v1
Abstract
We consider drawing statistical inferences based on data subject to non-Gaussian measurement error. Unlike most existing methods developed under the assumption of Gaussian measurement error, the proposed strategy exploits hypercomplex numbers to reduce bias in naive estimation that ignores non-Gaussian measurement error. We apply this new method to several widely applicable parametric regression models with error-prone covariates, and kernel density estimation using error-contaminated data. The efficacy of this method in bias reduction is demonstrated in simulation studies and a real-life application in sports analytics.
Cite
@article{arxiv.2505.02754,
title = {Debiased inference in error-in-variable problems with non-Gaussian measurement error},
author = {Nicholas W. Woolsey and Xianzheng Huang},
journal= {arXiv preprint arXiv:2505.02754},
year = {2025}
}