High-Dimensional Hettmansperger-Randles Estimator and Its Applications

ICLR 2026 Conference Submission15757 Authors

Keywords: Hettmansperger-Randles estimator, High-dimensional data, One-sample location test problem, Quadratic discriminant analysis, Spatial-sign

Abstract: The classic Hettmansperger-Randles estimator has found extensive use in robust statistical inference. However, it cannot be directly applied to high-dimensional data. In this paper, we propose a high-dimensional Hettmansperger-Randles estimator for the location parameter and scatter matrix of elliptical distributions in high-dimensional scenarios. Subsequently, we apply these estimators to two prominent problems: the one-sample location test problem and quadratic discriminant analysis. We discover that the corresponding new methods exhibit high effectiveness across a broad range of distributions. Both simulation studies and real-data applications further illustrate the superiority of the newly proposed methods.

Primary Area: probabilistic methods (Bayesian methods, variational inference, sampling, UQ, etc.)

Submission Number: 15757