Methods for estimating sparse and large covariance matrices Covariance and correlation matrices play fundamental roles in every aspect of the analysis of multiv

This book presents covariance matrix estimation and related aspects of random matrix theory. It focuses on the sample covariance matrix estimator and provides a

High-dimensional data appear in many fields, and their analysis has become increasingly important in modern statistics. However, it has long been observed that

Covering theory, algorithms, and methodologies, as well as data mining technologies, Data Mining for Bioinformatics provides a comprehensive discussion of data-

Estimation of large dispersion and autocovariance matrices using banding and tapering Joint convergence of high dimensional generalized dispersion matrices Limi