I. M. Johnstone, On the distribution of the largest eigenvalue in principal components analysis, The Annals of statistics, vol.29, issue.2, pp.295-327, 2001.

P. Stoica and Y. Selen, Model-order selection: a review of information criterion rules, IEEE Signal Processing Magazine, vol.21, issue.4, pp.36-47, 2004.

M. E. Tipping and C. M. Bishop, Probabilistic principal component analysis, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.61, issue.3, pp.611-622, 1999.

B. Kang, V. Monga, and M. Rangaswamy, Rankconstrained maximum likelihood estimation of structured covariance matrices, IEEE Transactions on Aerospace and Electronic Systems, vol.50, issue.1, pp.501-515, 2014.

E. Ollila, D. E. Tyler, V. Koivunen, and H. V. Poor, Complex elliptically symmetric distributions: Survey, new results and applications, IEEE Transactions on signal processing, vol.60, issue.11, pp.5597-5625, 2012.

D. E. Tyler, A distribution-free m-estimator of multivariate scatter, The Annals of Statistics, p.234251, 1987.

F. Pascal, Y. Chitour, J. Ovarlez, P. Forster, and P. Larzabal, Covariance structure maximum-likelihood estimates in compound gaussian noise: Existence and algorithm analysis, IEEE Transactions on Signal Processing, vol.56, issue.1, pp.34-48, 2008.
URL : https://hal.archives-ouvertes.fr/hal-01816367

Y. Sun, P. Babu, and D. P. Palomar, Robust estimation of structured covariance matrix for heavy-tailed elliptical distributions, IEEE Transactions on Signal Processing, vol.64, issue.14, pp.3576-3590, 2016.

S. Bonnabel and R. Sepulchre, Riemannian metric and geometric mean for positive semidefinite matrices of fixed rank, SIAM Journal on Matrix Analysis and Applications, vol.31, issue.3, pp.1055-1070, 2009.

B. Vandereycken and S. Vandewalle, A Riemannian optimization approach for computing low-rank solutions of Lyapunov equations, SIAM Journal on Matrix Analysis and Applications, vol.31, issue.5, pp.2553-2579, 2010.

G. Meyer, S. Bonnabel, and R. Sepulchre, Regression on fixed-rank positive semidefinite matrices: a Riemannian approach, Journal of Machine Learning Research, vol.12, pp.593-625, 2011.

B. Vandereycken, P. Absil, and S. Vandewalle, A Riemannian geometry with complete geodesics for the set of positive semidefinite matrices of fixed rank, IMA Journal of Numerical Analysis, vol.33, issue.2, pp.481-514, 2012.

E. Massart and P. , Quotient geometry with simple geodesics for the manifold of fixed-rank positivesemidefinite matrices, 2018.

P. Absil, R. Mahony, and R. Sepulchre, Optimization Algorithms on Matrix Manifolds, 2008.

A. Edelman, T. A. Arias, and S. T. Smith, The geometry of algorithms with orthogonality constraints, SIAM journal on Matrix Analysis and Applications, vol.20, issue.2, pp.303-353, 1998.

A. Breloy, G. Ginolhac, A. Renaux, and F. Bouchard, Intrinsic cramér-rao bounds for scatter and shape matrices estimation in ces distributions, IEEE Signal Processing Letters, vol.26, issue.2, pp.262-266, 2018.

N. Boumal, B. Mishra, P. Absil, and R. Sepulchre, Manopt, a Matlab toolbox for optimization on manifolds, Journal of Machine Learning Research, vol.15, pp.1455-1459, 2014.

P. Absil, R. Mahony, and R. Sepulchre, Riemannian geometry of Grassmann manifolds with a view on algorithmic computation, Acta Applicandae Mathematica, vol.80, issue.2, pp.199-220, 2004.