02182cam a22001818i 4500008004100000020001900041082001900060100001600079245004900095260002800144300001800172504005100190520165300241650002901894650001301923650002701936700003701963180608s2018 flu b 001 0 eng a9781138303867 a512.9434bB6L2 aBose, Arup, aLarge covariance and autocovariance matrices aLondonbCRC Pressc2019 axxiii, 272 p. aIncludes bibliographical references and index. 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 aMathematical statistics aMatrices aAnalysis of covariance aBhattacharjee, MonikaeCo author