120 citations · 545 across the 20 of their papers we have counts for
10 papers · 1 filter
Robust regularized covariance matrix estimation: well-posedness and convergent algorithm
Mengxi Yi, David Tyler
In this paper, we study properties of penalized and structured M-estimators of multivariate scatter, based on geodesically convex but not necessarily smooth penalty functions. Exis…
Robust and Resistant Regularized Covariance Matrices
David E. Tyler, Mengxi Yi, Klaus Nordhausen
We introduce a class of regularized M-estimators of multivariate scatter and show, analogous to the popular spatial sign covariance matrix (SSCM), that they possess high breakdown…
Linear pooling of sample covariance matrices
Elias Raninen, David E. Tyler, Esa Ollila
We consider the problem of estimating high-dimensional covariance matrices of -populations or classes in the setting where the sample sizes are comparable to the data dimension.…
Breakdown points of penalized and hybrid M-estimators of covariance
David E. Tyler, Mengxi Yi
We introduce a class of hybrid M-estimators of multivariate scatter which, analogous to the popular spatial sign covariance matrix (SSCM), possess high breakdown points. We also sh…
Lassoing Eigenvalues
David E. Tyler, Mengxi Yi
The properties of penalized sample covariance matrices depend on the choice of the penalty function. In this paper, we introduce a class of non-smooth penalty functions for the sam…
Asymptotic and bootstrap tests for subspace dimension
Klaus Nordhausen, Hannu Oja, David E. Tyler
Most linear dimension reduction methods proposed in the literature can be formulated using an appropriate pair of scatter matrices, see e.g. Ye and Weiss (2003), Tyler et al. (2009…