Year: 2025
Type: preprint
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.
Source: arXiv (Cornell University)
Authors: Woolsey, Nicholas W., Huang, Xianzheng
Institution: University of South Carolina
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4415032485
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