High-Dimensional Covariance Matrix Estimation: An Introduction to Random Matrix Theory (SpringerBriefs in Applied Statistics and Econometrics) - Image 1

Price loading...

Springer High-Dimensional Covariance Matrix Estimation: An Introduction to Random Matrix Theory (SpringerBriefs in Applied Statistics and Econometrics)

Name: High-Dimensional Covariance Matrix Estimation: An Introduction to Random Matrix Theory (SpringerBriefs in Applied Statistics and Econometrics)
Brand: Springer

Managers' Guides to Computing

Price History & Forecast

No Price Data Available

Price history will appear here once data is collected from Amazon.

Price Distribution

No price data available for histogram

Description

This book presents covariance matrix estimation and related aspects of random matrix theory. It focuses on the sample covariance matrix estimator and provides a holistic description of its properties under two asymptotic regimes: the traditional one, and the high-dimensional regime that better fits the big data context. It draws attention to the deficiencies of standard statistical tools when used in the high-dimensional setting, and introduces the basic concepts and major results related to spectral statistics and random matrix theory under high-dimensional asymptotics in an understandable and reader-friendly way. The aim of this book is to inspire applied statisticians, econometricians, and machine learning practitioners who analyze high-dimensional data to apply the recent developments in their work.

Product Specifications

Brand: Springer
Format: paperback
ASIN: 3030800644
Category: Books > Subjects > Business, Finance & Law > E-Commerce > Managers' Guides to Computing
Domain: Amazon UK
Release Date: 30 October 2021
Listed Since: 28 May 2021

Barcode

No barcode data available

Springer High-Dimensional Covariance Matrix Estimation: An Introduction to Random Matrix Theory (SpringerBriefs in Applied Statistics and Econometrics)

Price History & Forecast

No Price Data Available

Price Distribution

Description

Product Specifications

Barcode

Similar Products You Might Like

Springer Fundamentals of High-Dimensional Statistics Textbook

Fundamentals of High-Dimensional Statistics: With Exercises and R Labs (Springer Texts in Statistics)

Springer Matrix-Based Introduction to Multivariate Data Analysis

Matrix-Based Introduction to Multivariate Data Analysis

Statistics for High-Dimensional Data: Methods, Theory and Applications (Springer Series in Statistics)

Statistics for High-Dimensional Data: Methods, Theory and Applications (Springer Series in Statistics)

Big and Complex Data Analysis: Methodologies and Applications (Contributions to Statistics)

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

High-Dimensional Statistics: A Non-Asymptotic Viewpoint: 48 (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 48)

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

Introduction to High-Dimensional Statistics (Chapman & Hall/CRC Monographs on Statistics and Applied Probability)

Random Matrix Methods for Machine Learning

Matrix-Based Introduction to Multivariate Data Analysis

High-Dimensional Probability: An Introduction with Applications in Data Science: 47 (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 47)

Multivariate Statistics: High-Dimensional and Large-Sample Approximations (Wiley Series in Probability and Statistics)

Random Matrices and Non-Commutative Probability

Applied Probability: From Random Experiments to Random Sequences and Statistics

A First Course in Random Matrix Theory: for Physicists, Engineers and Data Scientists

Matrix Algebra: Theory, Computations, and Applications in Statistics (Springer Texts in Statistics)

An Introduction to Statistical Analysis of Random Arrays

Stochastic Finite Elements: A Spectral Approach

Applied Multivariate Statistical Analysis

Econometrics of Financial High-Frequency Data

Econometrics of Financial High-Frequency Data