Covariance estimation in time varying ARMA processes

Ami Wiesel, Amir Globerson

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We consider large scale covariance estimation using a small number of samples in applications where there is a natural ordering between the random variables. The two classical approaches to this problem rely on banded covariance and banded inverse covariance structures, corresponding to time varying moving average (MA) and autoregressive (AR) models, respectively. Motivated by this analogy to spectral estimation and the well known modeling power of autoregressive moving average (ARMA) processes, we propose a novel time varying ARMA covariance structure. Similarly to known results in the context of AR and MA, we address the completion of an ARMA covariance matrix from its main band, and its estimation based on random samples. Finally, we examine the advantages of our proposed methods using numerical experiments.

Original languageEnglish
Title of host publication2012 IEEE 7th Sensor Array and Multichannel Signal Processing Workshop, SAM 2012
Pages357-360
Number of pages4
DOIs
StatePublished - 2012
Externally publishedYes
Event2012 IEEE 7th Sensor Array and Multichannel Signal Processing Workshop, SAM 2012 - Hoboken, NJ, United States
Duration: 17 Jun 201220 Jun 2012

Publication series

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

Conference

Conference2012 IEEE 7th Sensor Array and Multichannel Signal Processing Workshop, SAM 2012
Country/TerritoryUnited States
CityHoboken, NJ
Period17/06/1220/06/12

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