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Large Covariance and Autocovariance Matrices (Chapman & Hall/CRC Monographs on Statistics and Applied Probability) by Bhattacharjee, Monika; Bose, Arup is a nonfiction available to read on EtoBox.

What is Large Covariance and Autocovariance Matrices (Chapman & Hall/CRC Monographs on Statistics and Applied Probability) about?

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) a

Who reads Large Covariance and Autocovariance Matrices (Chapman & Hall/CRC Monographs on Statistics and Applied Probability)?

Author

Bhattacharjee, Monika; Bose, Arup

Publisher

Chapman and Hall/CRC, an imprint of Taylor and Francis

Published

2018

Language

EN

ISBN

9780367734107

Category

nonfiction

Subjects

Mathematics, Science, Stem