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arXiv · 2505.02754

Debiased inference in error-in-variable problems with non-Gaussian measurement error

Nicholas W. Woolsey·Xianzheng Huang
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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.

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Nicholas W. Woolsey, Xianzheng Huang. 2025-05-05. Debiased inference in error-in-variable problems with non-Gaussian measurement error. https://arxiv.org/abs/2505.02754

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