High-Dimensional Covariance Estimation

"Focusing on methodology and computation more than on theorems and proofs, this book provides computationally feasible and statistically efficient methods for estimating sparse and large covariance matrices of high-dimensional data. Extensive in breadth and scope, it features ample applications to a number of applied areas, including business and economics, computer science, engineering, and financial mathematics; recognizes the important and significant contributions of longitudinal and spatial data; and includes various computer codes in R throughout the text and on an author-maintained web site"--

MOHSEN POURAHMADI, PhD, is Professor of Statistics at Texas A&M University. He is an elected member of the International Statistical Institute, a Fellow of the American Statistical Association, and a member of the American Mathematical Society. Dr. Pourahmadi is the author of Foundations of Time Series Analysis and Prediction Theory, also published by Wiley.