Automatic diagonal loading for Tyler's robust covariance estimator

Teng Zhang; Ami Wiesel

doi:10.1109/SSP.2016.7551741

Automatic diagonal loading for Tyler's robust covariance estimator

Teng Zhang, Ami Wiesel

The Rachel and Selim Benin School of Engineering and Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

14 Scopus citations

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.

Original language	English
Title of host publication	2016 19th IEEE Statistical Signal Processing Workshop, SSP 2016
Publisher	IEEE Computer Society
ISBN (Electronic)	9781467378024
DOIs	https://doi.org/10.1109/SSP.2016.7551741
State	Published - 24 Aug 2016
Event	19th IEEE Statistical Signal Processing Workshop, SSP 2016 - Palma de Mallorca, Spain Duration: 25 Jun 2016 → 29 Jun 2016

Publication series

Name	IEEE Workshop on Statistical Signal Processing Proceedings
Volume	2016-August

Conference

Conference	19th IEEE Statistical Signal Processing Workshop, SSP 2016
Country/Territory	Spain
City	Palma de Mallorca
Period	25/06/16 → 29/06/16

Bibliographical note

Publisher Copyright:
© 2016 IEEE.

Keywords

Robust estimation
high-dimensional statistics

Access to Document

10.1109/SSP.2016.7551741

Cite this

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title = "Automatic diagonal loading for Tyler's robust covariance estimator",

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.",

keywords = "Robust estimation, high-dimensional statistics",

author = "Teng Zhang and Ami Wiesel",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 19th IEEE Statistical Signal Processing Workshop, SSP 2016 ; Conference date: 25-06-2016 Through 29-06-2016",

year = "2016",

month = aug,

day = "24",

doi = "10.1109/SSP.2016.7551741",

language = "אנגלית",

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Zhang, T & Wiesel, A 2016, Automatic diagonal loading for Tyler's robust covariance estimator. in 2016 19th IEEE Statistical Signal Processing Workshop, SSP 2016., 7551741, IEEE Workshop on Statistical Signal Processing Proceedings, vol. 2016-August, IEEE Computer Society, 19th IEEE Statistical Signal Processing Workshop, SSP 2016, Palma de Mallorca, Spain, 25/06/16. https://doi.org/10.1109/SSP.2016.7551741

Automatic diagonal loading for Tyler's robust covariance estimator. / Zhang, Teng; Wiesel, Ami.
2016 19th IEEE Statistical Signal Processing Workshop, SSP 2016. IEEE Computer Society, 2016. 7551741 (IEEE Workshop on Statistical Signal Processing Proceedings; Vol. 2016-August).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Automatic diagonal loading for Tyler's robust covariance estimator

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Keywords

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