Large Covariance and Autocovariance Matrices free ebook download.

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Large Covariance and Autocovariance Matrices free ebook download

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Mathematics

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Catalogue

Author(s):	Arup Bose and Monika Bhattacharjee

Date:	2017	Format:	PDF	Language:	English	ISBN/ASIN:	1138303860
Pages:	1	OCR:		Quality:		ISBN13:
Uploader:	alex21s	Upload Date:	8/22/2018 6:07:01 PM

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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.

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	Title	Link	Password	Size (MB)	Thank to	Inform Admin
2249188	Book	http://tezfiles.com/file/021d37b077619...		23.00	alex21s

Tags: Covariance Large Matrices

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