Errors-in-variables regression for mixed Euclidean and non-Euclidean predictors

Jeong Min Jeon

Journal of Nonparametric Statistics, 2025, vol. 37, issue 2, 282-318

Abstract: In this paper, we explore a novel regression problem encompassing both Euclidean and non-Euclidean predictors, all of which are subject to measurement errors. Specifically, we focus on a non-Euclidean predictor taking values in a compact and connected Lie group. We propose a nonparametric estimator and establish its asymptotic properties, including rates of convergence and an asymptotic distribution. We validate the practical efficacy of our estimator through simulation studies and real data analysis.

Date: 2025
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DOI: 10.1080/10485252.2024.2378897

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