7 citations · 11 across the 8 of their papers we have counts for
8 papers
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…
Stab-GKnock: Controlled variable selection for partially linear models using generalized knockoffs
Han Su, Panxu Yuan, Qingyang Sun +2
The recently proposed fixed-X knockoff is a powerful variable selection procedure that controls the false discovery rate (FDR) in any finite-sample setting, yet its theoretical ins…
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…
Test of the Latent Dimension of a Spatial Blind Source Separation Model
Christoph Muehlmann, François Bachoc, Klaus Nordhausen +1
We assume a spatial blind source separation model in which the observed multivariate spatial data is a linear mixture of latent spatially uncorrelated Gaussian random fields contai…
On Cokriging, Neural Networks, and Spatial Blind Source Separation for Multivariate Spatial Prediction
Christoph Muehlmann, Klaus Nordhausen, Mengxi Yi
Multivariate measurements taken at irregularly sampled locations are a common form of data, for example in geochemical analysis of soil. In practical considerations predictions of…
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…
Shrinking the Sample Covariance Matrix using Convex Penalties on the Matrix-Log Transformation
David E. Tyler, Mengxi Yi
For -dimensional data, penalized versions of the sample covariance matrix are important when the sample size is small or modest relative to . Since the negative log-likelihoo…
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…