BACKGROUND: Desmosomal gene variants (DGVs) have been associated with a diverse spectrum of phenotypic manifestations within ...

This paper studies the optimal estimation of high-dimensional covariance matrices ... sections of this paper are as follows: Section 2 will provide the associated concepts and knowledge of covariance ...

The finding of reliable "excursions" from low-dimensional network states that ... because the difference between two covariance matrices is not necessarily a covariance matrix), we must adopt another ...

cov1Para: Linear shrinkage towards one-parameter matrix; all the variances are the same, all the covariances are zero. See Ledoit and Wolf (2004b). cov2Para: Linear shrinkage towards two-parameter ...

We give the first efficient algorithm for estimating the parameters of a high-dimensional ... robustly learn the covariance. Suppose the mean is zero and consider the following convex set C τ, where Σ ...

Abstract: Two-dimensional principal component analysis (2DPCA) is widely used in many applications, especially, face recognition. A key factor to improve the performance of the 2DPCA method comes from ...

we propose a new method for two-dimensional (2-D) direction-of-arrival (DOA) estimation using two parallel nested arrays. In this method, an augmented covariance matrix is firstly constructed using ...

High-dimensional data from molecular biology ... The purpose of this paper is two fold. First, we investigate three methods for generating constrained covariance matrices with a biologically realistic ...