10 citations · 13 across the 5 of their papers we have counts for
6 papers · 1 filter
Shrinking the eigenvalues of M-estimators of covariance matrix
Esa Ollila, Daniel P. Palomar, Frédéric Pascal
A highly popular regularized (shrinkage) covariance matrix estimator is the shrinkage sample covariance matrix (SCM) which shares the same set of eigenvectors as the SCM but shrink…
Robust Semiparametric DOA Estimation in non-Gaussian Environment
Stefano Fortunati, Alexandre Renaux, Frédéric Pascal
A general non-Gaussian semiparametric model is adopted to characterize the measurement vectors, i.e.\ the \textit{snapshots}, collected by a linear array. Moreover, the recently de…
M-estimators of scatter with eigenvalue shrinkage
Esa Ollila, Daniel P. Palomar, Frederic Pascal
A popular regularized (shrinkage) covariance estimator is the shrinkage sample covariance matrix (SCM) which shares the same set of eigenvectors as the SCM but shrinks its eigenval…
Properties of a new -estimator of shape matrices
Stefano Fortunati, Alexandre Renaux, Frédéric Pascal
This paper aims at presenting a simulative analysis of the main properties of a new -estimator of shape matrices in Complex Elliptically Symmetric (CES) distributed observations…
Robust Semiparametric Efficient Estimators in Elliptical Distributions
Stefano Fortunati, Alexandre Renaux, Frédéric Pascal
Covariance matrices play a major role in statistics, signal processing and machine learning applications. This paper focuses on the \textit{semiparametric} covariance/scatter matri…
A Riemannian Framework for Low-Rank Structured Elliptical Models
Florent Bouchard, Arnaud Breloy, Guillaume Ginolhac +2
This paper proposes an original Riemmanian geometry for low-rank structured elliptical models, i.e., when samples are elliptically distributed with a covariance matrix that has a l…