HALFSPACE DEPTHS FOR SCATTER, CONCENTRATION AND SHAPE MATRICES

• 成果类型: Article
• 署名作者: Paindaveine, Davy; Van Bever, Germain
• 署名单位: Universite Libre de Bruxelles; Universite Libre de Bruxelles
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/17-AOS1658
• 发表日期: 2018
• 页码: 3276-3307
• 关键词: multivariate; quantiles; CLASSIFICATION; inference; notion

摘要

We propose halfspace depth concepts for scatter, concentration and shape matrices. For scatter matrices, our concept is similar to those from Chen, Gao and Ren [Robust covariance and scatter matrix estimation under Huber's contamination model (2018)] and Zhang [J. Multivariate Anal. 82 (2002) 134-165]. Rather than focusing, as in these earlier works, on deepest scatter matrices, we thoroughly investigate the properties of the proposed depth and of the corresponding depth regions. We do so under minimal assumptions and, in particular, we do not restrict to elliptical distributions nor to absolutely continuous distributions. Interestingly, fully understanding scatter halfspace depth requires considering different geometries/topologies on the space of scatter matrices. We also discuss, in the spirit of Zuo and Serfling [Ann. Statist. 28 (2000) 461-482], the structural properties a scatter depth should satisfy, and investigate whether or not these are met by scatter half space depth. Companion concepts of depth for concentration matrices and shape matrices are also proposed and studied. We show the practical relevance of the depth concepts considered in a real-data example from finance.