Modern Methods to Covariance Estimation: With High-Dimensional Data

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.

Keywords: Multivariate Analysis, multivariate, multivariate statistics, statistics, mathematics, computer science, engineering, business, economics, multivariate analysis, covariance estimation, text-mining, statistical learning, stochastic, models, longitudinal data analysis, covariance matrix, data mining