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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月18日

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  • Nicholas Woolsey,
  • Xianzheng Huang

摘要

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.