Overview

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. Arup Bose is a professor at the Indian Statistical Institute, Kolkata, India. He is a distinguished researcher in mathematical statistics and has been working in high-dimensional random matrices for the last fifteen years. He has been editor of Sankhyā for several years and has been on the editorial board of several other journals. He is a Fellow of the Institute of Mathematical Statistics, USA and all three national science academies of India, as well as the recipient of the S.S. Bhatnagar Award and the C.R. Rao Award. His first book Patterned Random Matrices was also published by Chapman & Hall. He has a forthcoming graduate text U-statistics, M-estimates and Resampling (with Snigdhansu Chatterjee) to be published by Hindustan Book Agency. Monika Bhattacharjee is a post-doctoral fellow at the Informatics Institute, University of Florida. After graduating from St. Xavier's College, Kolkata, she obtained her master’s in 2012 and PhD in 2016 from the Indian Statistical Institute. Her thesis in high-dimensional covariance and auto-covariance matrices, written under the supervision of Dr. Bose, has received high acclaim.

Full Product Details

Author:   Arup Bose (Indian Statistical Institute, Kolkata) ,  Monika Bhattacharjee (Department of Mathematics, Indian Institute of Technology, Bombay, Powai, Mumbai, Maharashtra, India)
Publisher:   Taylor & Francis Ltd
Imprint:   CRC Press
Weight:   0.594kg
ISBN:  

9781138303867

ISBN 10:   1138303860
Pages:   272
Publication Date:   03 July 2018
Audience:   College/higher education ,  General/trade ,  Tertiary & Higher Education ,  General
Format:   Hardback
Publisher's Status:   Active
Availability:   In Print  
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Table of Contents

1. LARGE COVARIANCE MATRIX I Consistency Covariance classes and regularization Covariance classes Covariance regularization Bandable Σp Parameter space Estimation in U Minimaxity Toeplitz Σp Parameter space Estimation in Gβ (M ) or Fβ (M0, M ) Minimaxity Sparse Σp Parameter space Estimation in Uτ (q, C0(p), M ) or Gq (Cn,p) Minimaxity 2. LARGE COVARIANCE MATRIX II Bandable Σp Models and examples Weak dependence Estimation Sparse Σp 3. LARGE AUTOCOVARIANCE MATRIX Models and examples Estimation of Γ0,p Estimation of Γu,p Parameter spaces Estimation Estimation in MA(r) Estimation in IVAR(r) Gaussian assumption Simulations Part II 4. SPECTRAL DISTRIBUTION LSD Moment method Method of Stieltjes transform Wigner matrix: semi-circle law Independent matrix: Marˇcenko-Pastur law Results on Z: p/n → y > 0 Results on Z: p/n → 0 5. NON-COMMUTATIVE PROBABILITY NCP and its convergence Essentials of partition theory M¨obius function Partition and non-crossing partition Kreweras complement Free cumulant; free independence Moments of free variables Joint convergence of random matrices Compound free Poisson 6. GENERALIZED COVARIANCE MATRIX I Preliminaries Assumptions Embedding NCP convergence Main idea Main convergence LSD of symmetric polynomials Stieltjes transform Corollaries 7. GENERALIZED COVARIANCE MATRIX II Preliminaries Assumptions Centering and Scaling Main idea NCP convergence LSD of symmetric polynomials Stieltjes transform Corollaries 8. SPECTRA OF AUTOCOVARIANCE MATRIX I Assumptions LSD when p/n → y ∈ (0, ∞) MA(q), q < ∞ MA(∞) Application to specific cases LSD when p/n → 0 Application to specific cases Non-symmetric polynomials 9. SPECTRA OF AUTOCOVARIANCE MATRIX II Assumptions LSD when p/n → y ∈ (0, ∞) MA(q), q < ∞ MA(∞) LSD when p/n → 0 MA(q), q < ∞ MA(∞) 10. GRAPHICAL INFERENCE MA order determination AR order determination Graphical tests for parameter matrices 11. TESTING WITH TRACE One sample trace Two sample trace Testing 12. SUPPLEMENTARY PROOFS Proof of Lemma Proof of Theorem (a) Proof of Theorem Proof of Lemma Proof of Corollary (c) Proof of Corollary (c) Proof of Corollary (c) Proof of Lemma Proof of Lemma Lemmas for Theorem

Reviews

. . . the authors should be congratulated for producing two highly relevant and well-written books. Statisticians would probably gravitate to LCAM in the first instance and those working in linear algebra would probably gravitate to PRM. ~Jonathan Gillard, Cardiff University

"" . . . the authors should be congratulated for producing two highly relevant and well-written books. Statisticians would probably gravitate to LCAM in the first instance and those working in linear algebra would probably gravitate to PRM."" ~Jonathan Gillard, Cardiff University ""The book represents a monograph of the authors’ recent results about the theory of large covariance and autocovariance matrices and contains other important results from other research papers and books in this topic. It is very useful for all researchers who use large covariance and autocovariance matrices in their researches. Especially, it is very useful for post-graduate and PhD students in mathematics, statistics, econometrics and computer science. It is a well-written and organized book with a large number of solved examples and many exercises left to readers for homework. I would like to recommend the book to PhD students and researchers who want to learn or use large covariance and autocovariance matrices in their researches."" ~ Miroslav M. Ristic (Niš), zbMath ""This book brings together a collection of recent results on estimation of multidimensional time series covariance matrices. In the case where the time series consists of a sequence of independent (Chapter 1) or weakly dependent (Chapter 2) random vectors, the authors call it covariance estimation, whereas in the general case where the time series is only stationary, they call it autocovariance estimation. The framework of the results presented here is the one where the dimension of the observations (as well as the observation window size, otherwise nothing can be said) is high. The prerequisites include knowledge of elementary multivariate analysis, basic time series analysis, and basic results in stochastic convergence. In Chapter 1, the authors consider the case where we have at our disposal a large time series of iid high-dimensional observations with common covariance

. . . the authors should be congratulated for producing two highly relevant and well-written books. Statisticians would probably gravitate to LCAM in the first instance and those working in linear algebra would probably gravitate to PRM. ~Jonathan Gillard, Cardiff University This book brings together a collection of recent results on estimation of multidimensional time series covariance matrices. In the case where the time series consists of a sequence of independent (Chapter 1) or weakly dependent (Chapter 2) random vectors, the authors call it covariance estimation, whereas in the general case where the time series is only stationary, they call it autocovariance estimation. The framework of the results presented here is the one where the dimension of the observations (as well as the observation window size, otherwise nothing can be said) is high. The prerequisites include knowledge of elementary multivariate analysis, basic time series analysis, and basic results in stochastic convergence. In Chapter 1, the authors consider the case where we have at our disposal a large time series of iid high-dimensional observations with common covariance matrix C and want to estimate C. They provide some results on how to regularize the empirical sample covariance matrix in order to accurately estimate C in the case where C is either quickly decaying away from its diagonal (or \bendable ), Toeplitz or sparse. The regularization techniques involved are the \banding (zero-out of all entries above a certain distance from the diagonal), the tapering (instead of turning to zero, multiply by a factor which gets small as the distance to the diagonal gets large), and the thresholding (zero-out all entries smaller, in absolute value, than a certain well-chosen threshold). In Chapter 2, the same questions and techniques are discussed in the case where the independence between the observations is replaced by weak dependence. In Chapter 3, the authors suppress completely the hypothesis of independence of the observations, replace it by a stationarity hypothesis, and show how the techniques presented earlier allow one to still get estimations of the (auto)covariance matrix in the case of MA(r) and IVAR(r) models. Chapters 4 and 5 collect the basic concepts and results from respectively random matrix theory (RMT), about the empirical spectral distribution of various random matrix models, and Voiculescu's free probability theory that are needed in Chapters 6 to 10. Chapters 6 to 9, among other analogous questions, revisit the covariance matrix estimation results from Chapters 1 to 3 from the point of view of empirical spectral distribution (thanks to the framework defined in Chapters 4 and 5). In Chapter 10, it is demonstrated how the limiting spectral distribution (LSD) results obtained in Chapters 8 and 9 can be used in statistical graphical inference of high-dimensional time series. This includes estimation of unknown order of high-dimensional MA and AR processes. In Chapter 11, central limit theorems (CLTs) for linear spectral statistics of random matrices are used in signi cance tests for di erent hypotheses on coecient matrices. - Florent Benaych-Georges - Mathematical Reviews Clippings February 2019

Author Information

Arup Bose is a professor at the Indian Statistical Institute, Kolkata, India. He is a distinguished researcher in mathematical statistics and has been working in high-dimensional random matrices for the last fifteen years. He has been editor of Sankhyā for several years and has been on the editorial board of several other journals. He is a Fellow of the Institute of Mathematical Statistics, USA and all three national science academies of India, as well as the recipient of the S.S. Bhatnagar Award and the C.R. Rao Award. His first book Patterned Random Matrices was also published by Chapman & Hall. He has a forthcoming graduate text U-statistics, M-estimates and Resampling (with Snigdhansu Chatterjee) to be published by Hindustan Book Agency. Monika Bhattacharjee is a post-doctoral fellow at the Informatics Institute, University of Florida. After graduating from St. Xavier's College, Kolkata, she obtained her master’s in 2012 and PhD in 2016 from the Indian Statistical Institute. Her thesis in high-dimensional covariance and auto-covariance matrices, written under the supervision of Dr. Bose, has received high acclaim.

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