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Spectral theory of large dimensional random matrices and its applications to wireless communications and finance statistics : random matrix theory and its applications

[NT 42944] Record Type:	[NT 8598] Electronic resources : [NT 40817] monographic
[NT 47348] Title Information:	random matrix theory and its applications
[NT 47261] Author:	BaiZhidong,
[NT 47353] Alternative Intellectual Responsibility:	FangZhaoben,
[NT 47353] Alternative Intellectual Responsibility:	LiangYing-Chang,
[NT 47351] Place of Publication:	[Anhui]
[NT 47263] Published:	World Scientific; University of Science and Technology of China Press;
[NT 47352] Year of Publication:	2014
[NT 47264] Description:	1 online resource (xi, 220 p.)ill. :
[NT 47266] Subject:	Matrices -
[NT 51458] Online resource:	http://www.worldscientific.com/worldscibooks/10.1142/9049#t=toc
[NT 47265] Notes:	"This work is originally published by University of Science and Technology of China Press in 2010"--Title page verso
[NT 51398] Summary:	The book contains three parts: Spectral theory of large dimensional random matrices; Applications to wireless communications; and Applications to finance. In the first part, we introduce some basic theorems of spectral analysis of large dimensional random matrices that are obtained under finite moment conditions, such as the limiting spectral distributions of Wigner matrix and that of large dimensional sample covariance matrix, limits of extreme eigenvalues, and the central limit theorems for linear spectral statistics. In the second part, we introduce some basic examples of applications of
[NT 50961] ISBN:	9789814579063electronic bk.
[NT 50961] ISBN:	9814579068electronic bk.
[NT 60779] Content Note:	Limiting spectral distributions -- Extreme eigenvalues -- Central limit theorems of linear spectral statistics -- Limiting behavior of eigenmatrix of sample covariance matrix -- Wireless communications -- limiting performances of linear and iterative receivers -- Application to finance

Spectral theory of large dimensional random matrices and its applications to wireless communications and finance statistics : random matrix theory and its applications
Bai, Zhidong

Spectral theory of large dimensional random matrices and its applications to wireless communications and finance statistics : random matrix theory and its applications / Zhidong Bai, Zhaoben Fang, Ying-Chang Liang - [Anhui] : World Scientific, 2014. - 1 online resource (xi, 220 p.) ; ill..
Limiting spectral distributions -- Extreme eigenvalues -- Central limit theorems of linear spectral statistics -- Limiting behavior of eigenmatrix of sample covariance matrix -- Wireless communications -- limiting performances of linear and iterative receivers -- Application to finance.
"This work is originally published by University of Science and Technology of China Press in 2010"--Title page versoPrint version record.
Includes bibliographical references (p. 201-215) and index.
ISBN 9789814579063ISBN 9814579068
Matrices

Fang, Zhaoben

Spectral theory of large dimensional random matrices and its applications to wireless communications and finance statistics : random matrix theory and its applications
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300 $a "This work is originally published by University of Science and Technology of China Press in 2010"--Title page verso
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327 1 $a Limiting spectral distributions -- Extreme eigenvalues -- Central limit theorems of linear spectral statistics -- Limiting behavior of eigenmatrix of sample covariance matrix -- Wireless communications -- limiting performances of linear and iterative receivers -- Application to finance
330 $a The book contains three parts: Spectral theory of large dimensional random matrices; Applications to wireless communications; and Applications to finance. In the first part, we introduce some basic theorems of spectral analysis of large dimensional random matrices that are obtained under finite moment conditions, such as the limiting spectral distributions of Wigner matrix and that of large dimensional sample covariance matrix, limits of extreme eigenvalues, and the central limit theorems for linear spectral statistics. In the second part, we introduce some basic examples of applications of
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