國立虎尾科技大學 |

High-Dimensional Covariance Matrix Estimation = An Introduction to Random Matrix Theory /

紀錄類型:	書目-語言資料,印刷品 : 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.
面頁冊數:	XIV, 115 p. 26 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Machine Learning. -
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. - 1st ed. 2021. - XIV, 115 p. 26 illus. in color.online resource. - 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:

1137723
Machine Learning.

LC Class. No.: QA276-280

Dewey Class. No.: 330.015195

High-Dimensional Covariance Matrix Estimation = An Introduction to Random Matrix Theory /
LDR:02503nam a22004095i 4500 001 1056743
003 DE-He213
005 20211029074219.0
007 cr nn 008mamaa
008 220103s2021 sz | s |||| 0|eng d
020 $a 9783030800659 $9 978-3-030-80065-9
024 7 $a 10.1007/978-3-030-80065-9 $2 doi
035 $a 978-3-030-80065-9
050 4 $a QA276-280
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 330.015195 $2 23
100 1 $a Zagidullina, Aygul. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1362096
245 1 0 $a High-Dimensional Covariance Matrix Estimation $h [electronic resource] : $b An Introduction to Random Matrix Theory / $c by Aygul Zagidullina.
250 $a 1st ed. 2021.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2021.
300 $a XIV, 115 p. 26 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
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 2 4 $a Machine Learning. $3 1137723
650 2 4 $a Statistical Theory and Methods. $3 671396
650 2 4 $a Big Data. $3 1017136
650 1 4 $a Statistics for Business, Management, Economics, Finance, Insurance. $3 1211158
650 0 $a Machine learning. $3 561253
650 0 $a Big data. $3 981821
650 0 $a Econometrics. $3 556981
650 0 $a Statistics . $3 1253516
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783030800642
776 0 8 $i Printed edition: $z 9783030800666
830 0 $a SpringerBriefs in Applied Statistics and Econometrics, $x 2524-4124 $3 1362097
856 4 0 $u https://doi.org/10.1007/978-3-030-80065-9
912 $a ZDB-2-SMA
912 $a ZDB-2-SXMS
950 $a Mathematics and Statistics (SpringerNature-11649)
950 $a Mathematics and Statistics (R0) (SpringerNature-43713)