When the data are high dimensional, widely used multivariate statistical methods such as
principal component analysis can behave in unexpected ways. In settings where the …

A quote from the preface of the first edition:“Large-sample techniques provide solutions to
many practical problems; they simplify our solutions to difficult, sometimes intractable …

This book is aimed at students in communications and signal processing who want to extend
their skills in the energy area. It describes power systems and why these backgrounds are …

We propose a two-sample test for detecting the difference between mean vectors in a high-
dimensional regime based on a ridge-regularized Hotelling's T ². To choose the …

On testing for high-dimensional white noise Page 1 The Annals of Statistics 2019, Vol. 47, No.
6, 3382–3412 https://doi.org/10.1214/18-AOS1782 © Institute of Mathematical Statistics, 2019 …

A block Markov chain is a Markov chain whose state space can be partitioned into a finite
number of clusters such that the transition probabilities only depend on the clusters. Block …

Let {Zij} be independent and identically distributed (iid) random variables with EZij= 0, E| Zij|
²= 1 and E| Zij| ⁴<∞. Define linear processes Y tj=∑ k= 0∞ bk Z t− k, j with∑ i= 0∞| bi|<∞ …

In this paper we show that the empirical eigenvalue distribution of any sample covariance
matrix generated by independent samples of a stationary regular sequence has a limiting …

A recent empirical observation of activation sparsity in MLP layers offers an opportunity to
drastically reduce computation costs for free. Despite several works attributing it to training …

This is a selective review on the behavior of the high-dimensional sample covariance matrix,
S= n-1 XX∗, the most important random matrix in high-dimensional statistics, and some …