東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

High-dimensional covariance matrix e...

Zagidullina, Aygul.

FindBook

Google Book

Amazon

博客來

High-dimensional covariance matrix estimation = an introduction to random matrix theory /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	High-dimensional covariance matrix estimation/ by Aygul Zagidullina.
其他題名:	an introduction to random matrix theory /
作者:	Zagidullina, Aygul.
出版者:	Cham :Springer International Publishing : : 2021.,
面頁冊數:	xiv, 115 p. :ill., digital ;24 cm.
內容註:	Foreword -- 1 Introduction -- 2 Traditional Estimators and Standard Asymptotics -- 3 Finite Sample Performance of Traditional Estimators -- 4 Traditional Estimators and High-Dimensional Asymptotics -- 5 Summary and Outlook -- Appendices.
Contained By:	Springer Nature eBook
標題:	Random matrices. -
電子資源:	https://doi.org/10.1007/978-3-030-80065-9
ISBN:	9783030800659

High-dimensional covariance matrix estimation = an introduction to random matrix theory /
Zagidullina, Aygul.

High-dimensional covariance matrix estimationan introduction to random matrix theory /[electronic resource] :by Aygul Zagidullina. - Cham :Springer International Publishing :2021. - xiv, 115 p. :ill., digital ;24 cm. - SpringerBriefs in applied statistics and econometrics,2524-4124. - SpringerBriefs in applied statistics and econometrics..

Foreword -- 1 Introduction -- 2 Traditional Estimators and Standard Asymptotics -- 3 Finite Sample Performance of Traditional Estimators -- 4 Traditional Estimators and High-Dimensional Asymptotics -- 5 Summary and Outlook -- Appendices.

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.

ISBN: 9783030800659

Standard No.: 10.1007/978-3-030-80065-9doiSubjects--Topical Terms:

646309
Random matrices.

LC Class. No.: QA276.8 / .Z34 2021

Dewey Class. No.: 519.544

High-dimensional covariance matrix estimation = an introduction to random matrix theory /
LDR:02178nmm a2200349 a 4500 001 2253786
003 DE-He213
005 20211029074219.0
006 m d
007 cr nn 008maaau
008 220327s2021 sz s 0 eng d
020 $a 9783030800659 $q (electronic bk.)
020 $a 9783030800642 $q (paper)
024 7 $a 10.1007/978-3-030-80065-9 $2 doi
035 $a 978-3-030-80065-9
040 $a GP $c GP
041 0 $a eng
050 4 $a QA276.8 $b .Z34 2021
072 7 $a PBT $2 bicssc
072 7 $a BUS061000 $2 bisacsh
072 7 $a PBT $2 thema
072 7 $a K $2 thema
082 0 4 $a 519.544 $2 23
090 $a QA276.8 $b .Z18 2021
100 1 $a Zagidullina, Aygul. $3 3522327
245 1 0 $a High-dimensional covariance matrix estimation $h [electronic resource] : $b an introduction to random matrix theory / $c by Aygul Zagidullina.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2021.
300 $a xiv, 115 p. : $b ill., digital ; $c 24 cm.
490 1 $a SpringerBriefs in applied statistics and econometrics, $x 2524-4124
505 0 $a Foreword -- 1 Introduction -- 2 Traditional Estimators and Standard Asymptotics -- 3 Finite Sample Performance of Traditional Estimators -- 4 Traditional Estimators and High-Dimensional Asymptotics -- 5 Summary and Outlook -- Appendices.
520 $a 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.
650 0 $a Random matrices. $3 646309
650 0 $a Asymptotic efficiencies (Statistics) $3 647889
650 0 $a Multivariate analysis. $3 517467
650 1 4 $a Statistics for Business, Management, Economics, Finance, Insurance. $3 3382132
650 2 4 $a Econometrics. $3 542934
650 2 4 $a Big Data. $3 3134868
650 2 4 $a Statistical Theory and Methods. $3 891074
650 2 4 $a Machine Learning. $3 3382522
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a SpringerBriefs in applied statistics and econometrics. $3 3522328
856 4 0 $u https://doi.org/10.1007/978-3-030-80065-9
950 $a Mathematics and Statistics (SpringerNature-11649)