Multivariate Statistical Analysis: A High-Dimensional Approach (Theory and Decision Library B, 41, Band 41) - pocketboek

EAN (ISBN-13): 9789048155934
ISBN (ISBN-10): 9048155932
Gebonden uitgave
pocket book
Verschijningsjaar: 2010
Uitgever: Springer Netherlands Core >1
260 Bladzijden
Gewicht: 0,398 kg
Taal: eng/Englisch

Boek bevindt zich in het datenbestand sinds 2012-01-15T05:22:00+01:00 (Amsterdam)
Detailpagina laatst gewijzigd op 2023-11-07T22:42:30+01:00 (Amsterdam)
ISBN/EAN: 9789048155934

ISBN - alternatieve schrijfwijzen:
90-481-5593-2, 978-90-481-5593-4
alternatieve schrijfwijzen en verwante zoekwoorden:
Titel van het boek: theory and decision library, multivariate analysis, statistical analysis

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Gegevens van de uitgever

Auteur: V.I. Serdobolskii
Titel: Theory and Decision Library B; Multivariate Statistical Analysis - A High-Dimensional Approach
Uitgeverij: Springer; Springer Netherland
244 Bladzijden
Verschijningsjaar: 2010-12-09
Dordrecht; NL
Gedrukt / Gemaakt in
Taal: Engels
106,99 € (DE)
109,99 € (AT)
118,00 CHF (CH)
POD
XII, 244 p.

BC; Hardcover, Softcover / Mathematik/Wahrscheinlichkeitstheorie, Stochastik, Mathematische Statistik; Wahrscheinlichkeitsrechnung und Statistik; Verstehen; Estimator; Linear discriminant analysis; Radiologieinformationssystem; Regression analysis; Variance; calculus; econometrics; learning; linear regression; machine learning; mathematical statistics; quality; reliability; statistical analysis; statistics; quality control, reliability, safety and risk; Statistics; Security Science and Technology; Econometrics; Artificial Intelligence; Biostatistics; Sicherheitssysteme und Brandmeldeanlagen; Ökonometrie und Wirtschaftsstatistik; Künstliche Intelligenz; BB

Kolmogorov Asymptotics in Problems of Multivariate Analysis.- Spectral Theory of Large Covariance Matrices.- Approximately Unimprovable Essentially Multivariate Procedures.- 1. Spectral Properties of Large Wishart Matrices.- Wishart Distribution.- Limit Moments of Wishart Matrices.- Limit Formula for the Resolvent of Wishart Matrices.- 2. Resolvents and Spectral Functions of Large Sample Covariance Matrices.- Spectral Functions of Random Gram Matrices.- Spectral Functions of Sample Covariance Matrices.- Limit Spectral Functions of the Increasing Sample Covariance Matrices.- 3. Resolvents and Spectral Functions of Large Pooled Sample Covariance Matrices.- Problem Setting.- Spectral Functions of Pooled Random Gram Matrices.- Spectral Functions of Pooled Sample Covariance Matrices.- Limit Spectral Functions of the Increasing Pooled Sample Covariance Matrices.- 4. Normal Evaluation of Quality Functions.- Measure of Normalizability.- Spectral Functions of Large Covariance Matrices.- Normal Evaluation of Sample Dependent Functionals.- Discussion.- 5. Estimation of High-Dimensional Inverse Covariance Matrices.- Shrinkage Estimators of the Inverse Covariance Matrices.- Generalized Ridge Estimators of the Inverse Covariance Matrices.- Asymptotically Unimprovable Estimators of the Inverse Covariance Matrices.- 6. Epsilon-Dominating Component-Wise Shrinkage Estimators of Normal Mean.- Estimation Function for the Component-Wise Estimators.- Estimators of the Unimprovable Estimation Function.- 7. Improved Estimators of High-Dimensional Expectation Vectors.- Limit Quadratic Risk for a Class of Estimators of Expectation Vectors.- Minimization of the Limit Quadratic Risk.- Statistics to Approximate the Limit Risk Function.- Statistics to Approximate the Extremal limit Solution.- 8. Quadratic Risk of Linear Regression with a Large Number of Random Predictors.- Spectral Functions of Sample Covariance Matrices.- Functionals Depending on the Statistics Sand ?0.- Functionals Depending on Sample Covariance Matrices and Covariance Vectors.- The Leading Part of the Quadratic Risk and its Estimator.- Special Cases.- 9. Linear Discriminant Analysis of Normal Populations with Coinciding Covariance Matrices.- Problem Setting.- Expectation and Variance of Generalized Discriminant Functions.- Limit Probabilities of the Discrimination Errors.- 10. Population Free Quality of Discrimination.- Problem Setting.- Leading Parts of Functionals for Normal Populations.- Leading Parts of Functionals for Arbitrary Populations.- Discussion.- Proofs.- 11. Theory of Discriminant Analysis of the Increasing Number of Independent Variables.- Problem Setting.- A Priori Weighting of Independent Variables.- Minimization of the Limit Error Probability for a Priori Weighting.- Weighting of Independent Variables by Estimators.- Minimization of the Limit Error Probability for Weighting by Estimators.- Statistics to Estimate Probabilities of Errors.- Contribution of Variables to Discrimination.- Selection of a Large Number of Independent Variables.- Conclusions.- References.