TY - BOOK
AU - Bose, Arup,
AU - Bhattacharjee, Monika
TI - Large covariance and autocovariance matrices
SN - 9781138303867
U1 - 512.9434
PY - 2019///
CY - London
PB - CRC Press
KW - Mathematical statistics
KW - Matrices
KW - Analysis of covariance
N1 - Includes bibliographical references and index
N2 - Large Covariance and Autocovariance Matrices brings together a collection of recent results on sample covariance and autocovariance matrices in high-dimensional models and novel ideas on how to use them for statistical inference in one or more high-dimensional time series models. The prerequisites include knowledge of elementary multivariate analysis, basic time series analysis and basic results in stochastic convergence.
Part I is on different methods of estimation of large covariance matrices and auto-covariance matrices and properties of these estimators. Part II covers the relevant material on random matrix theory and non-commutative probability. Part III provides results on limit spectra and asymptotic normality of traces of symmetric matrix polynomial functions of sample auto-covariance matrices in high-dimensional linear time series models. These are used to develop graphical and significance tests for different hypotheses involving one or more independent high-dimensional linear time series.
The book should be of interest to people in econometrics and statistics (large covariance matrices and high-dimensional time series), mathematics (random matrices and free probability) and computer science (wireless communication). Parts of it can be used in post-graduate courses on high-dimensional statistical inference, high-dimensional random matrices and high-dimensional time series models. It should be particularly attractive to researchers developing statistical methods in high-dimensional time series models.
https://www.crcpress.com/Large-Covariance-and-Autocovariance-Matrices/Bose-Bhattacharjee/p/book/9781138303867
ER -