LEADER 00760nam0-22003011i-450- 001 990003245510403321 005 20060206134940.0 035 $a000324551 035 $aFED01000324551 035 $a(Aleph)000324551FED01 035 $a000324551 100 $a20030910d1916----km-y0itay50------ba 101 0 $afre 102 $aFR 105 $ay-------001yy 200 1 $aChine du sud$eJava.Japon 210 $aParis$cHachette$d1916 215 $a514 p.$d18.5 cm 610 0 $aguida extraeuropea$aCina 676 $a046.005 710 02$aGuides Madrolle$0493869 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990003245510403321 952 $a046.005.MAD$b85$fDECGE 959 $aDECGE 996 $aChine du sud$9449932 997 $aUNINA LEADER 03241nam 22007095 450 001 9910508455703321 005 20250320005259.0 010 $a9783030800659 010 $a3030800652 024 7 $a10.1007/978-3-030-80065-9 035 $a(CKB)5470000001298882 035 $a(MiAaPQ)EBC6796340 035 $a(Au-PeEL)EBL6796340 035 $a(OCoLC)1286428618 035 $a(PPN)25830023X 035 $a(DE-He213)978-3-030-80065-9 035 $a(EXLCZ)995470000001298882 100 $a20211029d2021 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aHigh-Dimensional Covariance Matrix Estimation $eAn Introduction to Random Matrix Theory /$fby Aygul Zagidullina 205 $a1st ed. 2021. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2021. 215 $a1 online resource (123 pages) 225 1 $aSpringerBriefs in Applied Statistics and Econometrics,$x2524-4124 311 08$a9783030800642 311 08$a3030800644 327 $aForeword -- 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. 330 $aThis book presents covariance matrix estimation and related aspects of random matrix theory. It focuses on the sample covariance matrix estimator and provides a holistic description of its properties under two asymptotic regimes: the traditional one, and the high-dimensional regime that better fits the big data context. It draws attention to the deficiencies of standard statistical tools when used in the high-dimensional setting, and introduces the basic concepts and major results related to spectral statistics and random matrix theory under high-dimensional asymptotics in an understandable and reader-friendly way. The aim of this book is to inspire applied statisticians, econometricians, and machine learning practitioners who analyze high-dimensional data to apply the recent developments in their work. 410 0$aSpringerBriefs in Applied Statistics and Econometrics,$x2524-4124 606 $aStatistics 606 $aEconometrics 606 $aBig data 606 $aStatistics 606 $aMachine learning 606 $aStatistics in Business, Management, Economics, Finance, Insurance 606 $aEconometrics 606 $aBig Data 606 $aStatistical Theory and Methods 606 $aMachine Learning 615 0$aStatistics. 615 0$aEconometrics. 615 0$aBig data. 615 0$aStatistics. 615 0$aMachine learning. 615 14$aStatistics in Business, Management, Economics, Finance, Insurance. 615 24$aEconometrics. 615 24$aBig Data. 615 24$aStatistical Theory and Methods. 615 24$aMachine Learning. 676 $a512.9434 700 $aZagidullina$b Aygul$01071981 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910508455703321 996 $aHigh-Dimensional Covariance Matrix Estimation$92568135 997 $aUNINA