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Large Covariance and Autocovariance Matrices

Large Covariance and Autocovariance Matrices brings together a collection of recent results on sample covariance and autocovariance;

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Large Covariance and Autocovariance Matrices

Large Covariance and Autocovariance Matrices brings together a collection of recent results on sample covariance and autocovariance;

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Patterned Random Matrices

Large dimensional random matrices (LDRM) with specific patterns arise in econometrics, computer science, mathematics, physics, and;

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Patterned Random Matrices

Large dimensional random matrices (LDRM) with specific patterns arise in econometrics, computer science, mathematics, physics, and;

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High-Dimensional Covariance Estimation

Methods for estimating sparse and large covariance matrices Covariance and correlation matrices play fundamental roles in every aspect;

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Random Matrices and Non-Commutative Probability

This is an introductory book on Non-Commutative Probability or Free Probability and Large Dimensional Random Matrices. Basic concepts;

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Random Circulant Matrices

Circulant matrices have been around for a long time and have been extensively used in many scientific areas. This book studies the;

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Random Circulant Matrices

Circulant matrices have been around for a long time and have been extensively used in many scientific areas. This book studies the;

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Spectral Theory Of Large Dimensional Random Matrices And Its Applications To Wireless Communications And Finance Statistics

The book contains three parts: Spectral theory of large dimensional random matrices; Applications to wireless communications; and;

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Asymptotic Theory for Econometricians

This book provides the tools and concepts necessary to study the behavior of econometric estimators and test statistics in large samples;

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Shrinkage Estimation for Mean and Covariance Matrices

, it presents recent techniques and results in estimation of mean and covariance matrices with a high-dimensional setting that implies;

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Spectral Analysis of Large Dimensional Random Matrices

of eigenvectors of sample covariance matrices, another on applications to wireless communications and finance. While attempting to bring this edition up;

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A First Course in Random Matrix Theory

theory and the study of large sample covariance matrices can efficiently process these big data sets and help make sense of modern, deep learning;

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Linear Models And Regression With R

Starting with the basic linear model where the design and covariance matrices are of full rank, this book demonstrates how the same;

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Recent Developments in Multivariate and Random Matrix Analysis

papers. The contents of the papers evolve around multivariate analysis and random matrices with topics such as high-dimensional analysis;

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Linear Algebra for Pattern Processing

. Such pattern information processing deals with a large amount of data, which are represented by high-dimensional vectors and matrices. There;

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Random Matrices: High Dimensional Phenomena

This book focuses on the behaviour of large random matrices. Standard results are covered, and the presentation emphasizes elementary;

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Understanding Correlation Matrices

Hadd and Joseph Lee Rodgers cover many features of correlations matrices including statistical hypothesis tests, their role in factor analysis;

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Noncommutative Probability and Random Matrices at Saint Flour

matrices: Lectures on macroscopic asymptotics.;

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Hierarchical Matrices

populated large-scale matrices is usually rather costly. However, the technique of hierarchical matrices makes it possible to store matrices and to;

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Matrix Positivity

relations. The matrix classes emphasized in this book include the classes of semipositive matrices, P-matrices, inverse M-matrices, and copositive;

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Statistical Foundations of Data Science

Provides theoretical insights and justification of the statistical procedures for the analysis of high-dimensional data Presents a general;

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Free Probability and Random Matrices

relation of free probability to random matrices, but also touches upon the operator algebraic, combinatorial, and analytic aspects of the theory;

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Linear Models

models, mean vectors, covariance matrices and sums of squares matrices for balanced and unbalanced data sets. The author includes both applied;

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Matrix Analysis for Statistics

covariance matrices Matrix Analysis for Statistics, Third Edition is an ideal textbook for upper-undergraduate and graduate-level courses on matrix;

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Traffic Distributions and Independence

for random matrices and then defines the traffic distribution of random matrices, which is richer than the $^*$-distribution of free;

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Matrices and Determinoids

matrices and determinoids as distinguished from square matrices and determinants, the determinoid of a rectangular matrix being related to it;

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