# Robust Estimation of Structured Covariance Matrix for Heavy-Tailed Elliptical Distributions

arXiv: https://arxiv.org/abs/1506.05215 · HTML: https://mlsanity.com/paper/1506.05215/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jun 2015.

## Built on by (most cited first, 9 of 9)

- [The Fisher-Rao geometry of CES distributions](https://mlsanity.com/paper/2310.01032/) (2023)
- [Riemannian optimization for non-centered mixture of scaled Gaussian distributions](https://mlsanity.com/paper/2209.03315/) (2022)
- [Efficient Hierarchical Bayesian Inference for Spatio-temporal Regression Models in Neuroimaging](https://mlsanity.com/paper/2111.01692/) (2021)
- [Frank-Wolfe-based Algorithms for Approximating Tyler's M-estimator](https://mlsanity.com/paper/2206.09370/) (2022)
- [Learning minimal volume uncertainty ellipsoids](https://mlsanity.com/paper/2405.02441/) (2024)
- [Self-Supervised Learning for Covariance Estimation](https://mlsanity.com/paper/2403.08662/) (2024)
- [A Randomized Linearly Convergent Frank-Wolfe-type Method for Smooth Convex Minimization over the Spectrahedron](https://mlsanity.com/paper/2503.01441/) (2025)
- [T-Rex: Fitting a Robust Factor Model via Expectation-Maximization](https://mlsanity.com/paper/2505.12117/) (2025)
- [Tensor-normal maximum likelihood estimation at the operator-norm sample threshold](https://mlsanity.com/paper/2608.10488/) (2026)

## Abstract (arXiv)

This paper considers the problem of robustly estimating a structured covariance matrix with an elliptical underlying distribution with known mean. In applications where the covariance matrix naturally possesses a certain structure, taking the prior structure information into account in the estimation procedure is beneficial to improve the estimation accuracy. We propose incorporating the prior structure information into Tyler's M-estimator and formulate the problem as minimizing the cost function of Tyler's estimator under the prior structural constraint. First, the estimation under a general convex structural constraint is introduced with an efficient algorithm for finding the estimator derived based on the majorization minimization (MM) algorithm framework. Then, the algorithm is tailored to several special structures that enjoy a wide range of applications in signal processing related fields, namely, sum of rank-one matrices, Toeplitz, and banded Toeplitz structure. In addition, two types of non-convex structures, i.e., the Kronecker structure and the spiked covariance structure, are also discussed, where it is shown that simple algorithms can be derived under the guidelines of MM. Numerical results show that the proposed estimator achieves a smaller estimation error than the benchmark estimators at a lower computational cost.