000 01198cam a22003258i 4500
999 _c210711
008 180608s2018 flu b 001 0 eng
020 _a9781138303867
082 _a512.9434
100 _aBose, Arup,
245 _aLarge covariance and autocovariance matrices
260 _aLondon
_bCRC Press
300 _axxiii, 272 p.
504 _aIncludes bibliographical references and index.
520 _aLarge 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
650 _aMathematical statistics
650 _aMatrices
650 _aAnalysis of covariance
700 _aBhattacharjee, Monika
_eCo author
942 _2ddc