Automatic Diagonal Loading For Tyler'S Robust Covariance Estimator

Keywords

high-dimensional statistics; Robust estimation

Abstract

An approach of regularizing Tyler's robust M-estimator of the co-variance matrix is proposed. We also provide an automatic choice of the regularization parameter in the high-dimensional regime. Simulations show its advantage over the sample covariance estimator and Tyler's M-estimator when data is heavy-tailed and the number of samples is small. Compared with the previous approaches of regularizing Tyler's M-estimator, our approach has a similar performance and a much simpler way of choosing the regularization parameter automatically.

Publication Date

8-24-2016

Publication Title

IEEE Workshop on Statistical Signal Processing Proceedings

Volume

2016-August

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/SSP.2016.7551741

Socpus ID

84987892516 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84987892516

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