Large Covariance and Autocovariance Matrices

The material will be based on very recent advances in the theory and application of large covariance and autocovariance matrices. Technologies and methods in medical sciences, image processing, and other fields generate data where the dimension is large compared to the sample size and may also increase as the next set of measurements become available. Theoretical and practical study of such type of data has attracted recent attention of researchers since most of the methods in finite dimensional set up do not work in these cases, even asymptotically. This book will be mainly focused on the topics in high-dimensional situations.