In this paper, we focus on the estimation of a high-dimensional precision matrix. We propose a simple improvement of the graphical lasso framework (glasso) that is able to attain better statistical performance without sacrificing too much the computational cost. The proposed improvement is based...

It is well-known that if a random vector X is in the generalized domain of attraction of the multivariate normal law (GDAN), then all its components are in the domain of attraction of the normal law (DAN) and, moreover, the Euclidean inner products of X with all the nonrandom vectors of unit...

Based on an R2-valued random sample {(yi,xi),1≤i≤n} on the simple linear regression model yi=xiβ+α+εi with unknown error variables εi, least squares processes (LSPs) are introduced in D[0,1] for the unknown slope β and intercept α, as well as for the unknown β when α=0. These LSPs...