Regularized robust estimation of mean and covariance matrix under heavy tails and outliers

Ying Sun, Prabhu Babu, Daniel P. Palomar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper we consider the regularized mean and covariance estimation problem for samples drawn from elliptical family of distributions. The proposed estimator yields robust estimates when the underlying distribution is heavy-tailed or when there are outliers in the data samples. In the scenario that the number of samples is small, it shrinks the estimator of the mean and covariance towards arbitrary given prior targets. Numerical algorithms are designed for the estimator based on the majorization-minimization framework and the simulation shows that the proposed estimator achieves considerably better performance.

Original languageEnglish (US)
Title of host publication2014 IEEE 8th Sensor Array and Multichannel Signal Processing Workshop, SAM 2014
PublisherIEEE Computer Society
Pages125-128
Number of pages4
ISBN (Print)9781479914814
DOIs
StatePublished - 2014
Event2014 IEEE 8th Sensor Array and Multichannel Signal Processing Workshop, SAM 2014 - A Coruna, Spain
Duration: Jun 22 2014Jun 25 2014

Publication series

NameProceedings of the IEEE Sensor Array and Multichannel Signal Processing Workshop
ISSN (Electronic)2151-870X

Other

Other2014 IEEE 8th Sensor Array and Multichannel Signal Processing Workshop, SAM 2014
CountrySpain
CityA Coruna
Period6/22/146/25/14

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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