Shrinkage MMSE estimators of covariances beyond the zero-mean and stationary variance assumptions - Equipe Image, Modélisation, Analyse, GEométrie, Synthèse
Communication Dans Un Congrès Année : 2024

Shrinkage MMSE estimators of covariances beyond the zero-mean and stationary variance assumptions

Résumé

We tackle covariance estimation in low-sample scenarios, employing a structured covariance matrix with shrinkage methods. These involve convexly combining a low-bias/highvariance empirical estimate with a biased regularization estimator, striking a bias-variance trade-off. Literature provides optimal settings of the regularization amount through risk minimization between the true covariance and its shrunk counterpart. Such estimators were derived for zero-mean statistics with i.i.d. diagonal regularization matrices accounting for the average sample variance solely. We extend these results to regularization matrices accounting for the sample variances both for centered and noncentered samples. In the latter case, the empirical estimate of the true mean is incorporated into our shrinkage estimators. Introducing confidence weights into the statistics also enhance estimator robustness against outliers. We compare our estimators to other shrinkage methods both on numerical simulations and on real data to solve a detection problem in astronomy.
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Dates et versions

hal-04693522 , version 1 (10-09-2024)

Identifiants

  • HAL Id : hal-04693522 , version 1

Citer

Olivier Flasseur, Éric Thiébaut, Loïc Denis, Maud Langlois. Shrinkage MMSE estimators of covariances beyond the zero-mean and stationary variance assumptions. EUSIPCO 2024, Aug 2024, Lyon, France. ⟨hal-04693522⟩
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