Modern statistical methods for multivariate time series rely on the eigendecomposition of
matrix-valued functions such as time-varying covariance and spectral density matrices. The …

We consider the estimation of large covariance and precision matrices from high-
dimensional sub-Gaussian or heavier-tailed observations with slowly decaying temporal …

Large Covariance and Autocovariance Matrices brings together a collection of recent results
on sample covariance and autocovariance matrices in high-dimensional models and novel …

Large sample behaviour of high dimensional autocovariance matrices Page 1 The Annals of
Statistics 2016, Vol. 44, No. 2, 598–628 DOI: 10.1214/15-AOS1378 © Institute of Mathematical …

We study the problem of detecting a common change point in large panel data based on a
mean shift model, wherein the errors exhibit both temporal and cross-sectional dependence …

Suppose X is an N× n complex matrix whose entries are centered, independent, and
identically distributed random variables with variance 1∕ n and whose fourth moment is of …

This article derives some large sample properties of weighted l 1‐penalized corrected
quantile estimators of the regression parameter vector in a high‐dimensional errors in …

Supplement to “Joint convergence of sample autocovariance matrices when p/n→0 with
application.”. The supplementary file provides all technical details, free probability …

In this paper under some mild restrictions upper bounds on the rate of convergence for
estimators of p * pp× p autocovariance and precision matrices for high dimensional linear …

Abstract Model robustness has become increasingly popular in recent decades. We study
multiple aspects of robustness (in the setting of time series, image classification and linear …