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001440693 001__ 1440693
001440693 003__ OCoLC
001440693 005__ 20230309004656.0
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001440693 019__ $$a1282004755$$a1282596397$$a1283857184$$a1285168756$$a1287764836
001440693 020__ $$a9783030800659$$q(electronic bk.)
001440693 020__ $$a3030800652$$q(electronic bk.)
001440693 020__ $$z9783030800642
001440693 020__ $$z3030800644
001440693 0247_ $$a10.1007/978-3-030-80065-9$$2doi
001440693 035__ $$aSP(OCoLC)1281769955
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001440693 049__ $$aISEA
001440693 050_4 $$aQA276.8$$b.Z34 2021
001440693 08204 $$a519.5/44$$223
001440693 1001_ $$aZagidullina, Aygul,$$eauthor.
001440693 24510 $$aHigh-dimensional covariance matrix estimation :$$ban introduction to random matrix theory /$$cAygul Zagidullina.
001440693 264_1 $$aCham :$$bSpringer,$$c[2021]
001440693 264_4 $$c©2021
001440693 300__ $$a1 online resource :$$bcolor illustrations
001440693 336__ $$atext$$btxt$$2rdacontent
001440693 337__ $$acomputer$$bc$$2rdamedia
001440693 338__ $$aonline resource$$bcr$$2rdacarrier
001440693 347__ $$atext file
001440693 347__ $$bPDF
001440693 4901_ $$aSpringerBriefs in applied statistics and econometrics,$$x2524-4124
001440693 504__ $$aIncludes bibliographical references.
001440693 5050_ $$aForeword -- 1 Introduction -- 2 Traditional Estimators and Standard Asymptotics -- 3 Finite Sample Performance of Traditional Estimators -- 4 Traditional Estimators and High-Dimensional Asymptotics -- 5 Summary and Outlook -- Appendices.
001440693 506__ $$aAccess limited to authorized users.
001440693 520__ $$aThis book presents covariance matrix estimation and related aspects of random matrix theory. It focuses on the sample covariance matrix estimator and provides a holistic description of its properties under two asymptotic regimes: the traditional one, and the high-dimensional regime that better fits the big data context. It draws attention to the deficiencies of standard statistical tools when used in the high-dimensional setting, and introduces the basic concepts and major results related to spectral statistics and random matrix theory under high-dimensional asymptotics in an understandable and reader-friendly way. The aim of this book is to inspire applied statisticians, econometricians, and machine learning practitioners who analyze high-dimensional data to apply the recent developments in their work.
001440693 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed November 15, 2021).
001440693 650_0 $$aRandom matrices.
001440693 650_0 $$aAsymptotic efficiencies (Statistics)
001440693 650_0 $$aMultivariate analysis.
001440693 650_6 $$aMatrices aléatoires.
001440693 650_6 $$aEfficacité asymptotique (Statistique)
001440693 650_6 $$aAnalyse multivariée.
001440693 655_0 $$aElectronic books.
001440693 77608 $$iPrint version:$$z3030800644$$z9783030800642$$w(OCoLC)1253472760
001440693 830_0 $$aSpringerBriefs in applied statistics and econometrics.$$x2524-4124
001440693 852__ $$bebk
001440693 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-80065-9$$zOnline Access$$91397441.1
001440693 909CO $$ooai:library.usi.edu:1440693$$pGLOBAL_SET
001440693 980__ $$aBIB
001440693 980__ $$aEBOOK
001440693 982__ $$aEbook
001440693 983__ $$aOnline
001440693 994__ $$a92$$bISE