Work

Debiased inference in errors-in-variables problems with non-Gaussian measurement error

Year: 2026

Type: article

Abstract: We consider drawing statistical inferences based on data subject to non-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.

Source: Annals of the Institute of Statistical Mathematics

DOI: 10.1007/s10463-026-00984-x

Authors: Nicholas Woolsey, Xianzheng Huang

Institution: University of South Carolina

Language: English

Topic: Statistical Methods in Epidemiology

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: hybrid

Funder: University of South Carolina

OpenAlex ID: W7165124100