Intrinsic Data Depth for Hermitian Positive Definite Matrices

Joris Chau, Hernando Ombao, Rainer von Sachs

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Nondegenerate covariance, correlation, and spectral density matrices are necessarily symmetric or Hermitian and positive definite. This article develops statistical data depths for collections of Hermitian positive definite matrices by exploiting the geometric structure of the space as a Riemannian manifold. The depth functions allow one to naturally characterize most central or outlying matrices, but also provide a practical framework for inference in the context of samples of positive definite matrices. First, the desired properties of an intrinsic data depth function acting on the space of Hermitian positive definite matrices are presented. Second, we propose two pointwise and integrated data depth functions that satisfy each of these requirements and investigate several robustness and efficiency aspects. As an application, we construct depth-based confidence regions for the intrinsic mean of a sample of positive definite matrices, which is applied to the exploratory analysis of a collection of covariance matrices in a multicenter clinical trial. Supplementary materials and an accompanying R-package are available online.
Original languageEnglish (US)
Pages (from-to)427-439
Number of pages13
JournalJournal of Computational and Graphical Statistics
Volume28
Issue number2
DOIs
StatePublished - Nov 28 2018

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