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Serfling, R. (2006) Depth Functions in Nonparametric Multivariate Inference. In: Liu, R.Y., Serfling, R. and Souvaine, D.L., Eds., Data Depth: Robust Multivariate Analysis, Computational Geometry and Applications, American Mathematical Society, 1-16. https://doi.org/10.1090/dimacs/072/01

has been cited by the following article:

• TITLE: General Depth-Based Trimmed Means and Trimmed Scatter Matrices

  AUTHORS: Jin Wang

  KEYWORDS: Multivariate Trimmed Mean, Trimmed Scatter Matrix, Asymptotic Distribution, Asymptotic Efficiency, Robustness

  JOURNAL NAME: Applied Mathematics, Vol.17 No.7, July 31, 2026

  ABSTRACT: Multivariate descriptive measures for location and scatter are the foundation of multivariate statistics and underpin many methods in the field. In this paper, we propose and study some new general depth-based trimmed means and trimmed scatter matrices. In addition to the basic properties and algorithms, we establish the asymptotic distributions of their sample versions. Using the asymptotic distributions, we study the asymptotic efficiencies of the sample trimmed means and the sample trimmed scatter matrices. Robustness is explored through finite-sample breakdown point. The results show that the sample trimmed means and trimmed scatter matrices are not only highly efficient but also exceptionally robust, making them very favorable estimators for multivariate location and scatter.