Tyler's Covariance Matrix Estimator in Elliptical Models With Convex Structure

• Published in: IEEE transactions on signal processing Vol. 62; no. 20; pp. 5251 - 5259
• Main Authors: Soloveychik, Ilya, Wiesel, Ami
• Format: Journal Article
• Language: English
• Published: New York IEEE 15.10.2014; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Asymptotic properties; Banded structure; Computer aided engineering; Covariance; Covariance matrices; Elliptical distribution; Estimators; Generalized method of moments; Mathematical analysis; Optimization; Robust control; robust covariance estimation; Simulation; Transaction processing; Tyler's scatter estimator
• ISSN: 1053-587X; 1941-0476
• DOI: 10.1109/TSP.2014.2348951

We address structured covariance estimation in elliptical distributions by assuming that the covariance is a priori known to belong to a given convex set, e.g., the set of Toeplitz or banded matrices. We consider the General Method of Moments (GMM) optimization applied to robust Tyler's scatter M-estimator subject to these convex constraints. Unfortunately, GMM turns out to be non-convex due to the objective. Instead, we propose a new COCA estimator-a convex relaxation which can be efficiently solved. We prove that the relaxation is tight in the unconstrained case for a finite number of samples, and in the constrained case asymptotically. We then illustrate the advantages of COCA in synthetic simulations with structured compound Gaussian distributions. In these examples, COCA outperforms competing methods such as Tyler's estimator and its projection onto the structure set.