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High-Dimensional Covariance Matrix Estimation

setting, and introduces the basic concepts and major results related to spectral statistics and random matrix theory under high-dimensional;

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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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High-Dimensional Covariance Estimation

environmental and physical sciences. High-Dimensional Covariance Estimation provides accessible and comprehensive coverage of the classical and modern;

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Large Covariance and Autocovariance Matrices

and asymptotic normality of traces of symmetric matrix polynomial functions of sample auto-covariance matrices in high-dimensional linear time;

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Large Covariance and Autocovariance Matrices

and asymptotic normality of traces of symmetric matrix polynomial functions of sample auto-covariance matrices in high-dimensional linear time;

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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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High-Dimensional Probability

High-dimensional probability offers insight into the behavior of random vectors, random matrices, random subspaces, and objects used to;

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High-dimensional Data Analysis

Over the last few years, significant developments have been taking place in high-dimensional data analysis, driven primarily by a wide;

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

obtained under finite moment conditions, such as the limiting spectral distributions of Wigner matrix and that of large dimensional sample;

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Estimation, Inference and Specification Analysis

of likelihood-based methods of statistical estimation and interference. Professor White first explores the underlying motivation for maximum-likelihood;

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Covariance Analysis for Seismic Signal Processing

matrix analysis in the processing of geophysical signals. A background of basic linear algebra, statistics, and fundamental random signal;

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Patterned Random Matrices

in high-dimensional random matrices for the last fifteen years. He has been the Editor of Sankyha for several years and has been on the editorial;

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Patterned Random Matrices

in high-dimensional random matrices for the last fifteen years. He has been the Editor of Sankyha for several years and has been on the editorial;

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Estimation, Inference and Specification Analysis

of likelihood-based methods of statistical estimation and interference. Professor White first explores the underlying motivation for maximum-likelihood;

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Statistical Inference in Multifractal Random Walk Models for Financial Time Series

The dynamics of financial returns varies with the return period, from high-frequency data to daily, quarterly or annual data. Multifractal;

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Concentration of Maxima and Fundamental Limits in High-Dimensional Testing and Inference

This book provides a unified exposition of some fundamental theoretical problems in high-dimensional statistics. It specifically considers;

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Perspectives on Big Data Analysis

estimation for an array of interesting models. The book also presents some surprising results on high-dimensional data analysis, especially when;

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Factorization Methods for Discrete Sequential Estimation

This estimation reference text thoroughly describes matrix factorization methods successfully employed by numerical analysts, familiarizing;

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The Classification of Data under Autoregressive Circulant Covariance

such as geophysical and signals processing there is a natural structure on the variance-covariance matrix of the observation vectors. The;

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Spectral Analysis of Signals

, and can provide high resolution and low sidelobes. In this book, we present these algorithms for both one-dimensional and two-dimensional;

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Separating Information Maximum Likelihood Method for High-Frequency Financial Data

econometrics. Considerable interest has been given to the estimation problem of integrated volatility and covariance by using high-frequency financial;

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The Coordinate-Free Approach to Linear Models

regression models with non-random predictors in a finite-dimensional setting. This approach is more insightful, more elegant, more direct, and;

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Asymptotic Theory for Econometricians

/>* Central limit theory * Asymptotically efficient instrumental variables estimation* Estimation of asymptotic covariance matrices

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Deep Learning and Missing Data in Engineering Systems

estimate the missing data in high-dimensional data settings more accurately. Swarm intelligence algorithms are applied to address critical;

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Understanding Least Squares Estimation and Geomatics Data Analysis

Provides a modern approach to least squares estimation and data analysis for undergraduate land surveying and geomatics programs Rich;

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Random Fields Estimation

This book contains a novel theory of random fields estimation of Wiener type, developed originally by the author and presented here. No;

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