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

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.