Pourahmadi (55 resultados)

hardcover. Condición: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority.

Hardcover. Condición: Gut. 208 pp. Sun-bleached at the spine, name on endpaper, otherwise very well preserved copy 329 Sprache: Englisch Gewicht in Gramm: 568.

HRD. Condición: New. New Book. Shipped from UK. Established seller since 2000.

Condición: As New. Unread book in perfect condition.

Condición: New.

Condición: new.

Condición: New. pp. 208.

Condición: New. pp. 208.

Condición: New.

Condición: New.

Condición: As New. Unread book in perfect condition.

Hardcover. Condición: Fine. 1st Edition. Preface says that this "book provides a solid foundation for time series analysis and prediction theory . interdisciplinary nature of time series analysis lying at the intersection of the mathematical, statistical, computational, physical, engineering and system sciences. The goal is to join the two lines of developments in time series analysis centered around the work of Schuster, Yule, Slutsky, Wold, . and prediction theory base on the work of Szego, Wold, Cramer, Kolmororov, Wiener, . into a logically sound and pedagogically reasonable theory of modern statistical time series analysis.".…

Hardback. Condición: New. Methods for estimating sparse and large covariance matrices Covariance and correlation matrices play fundamental roles in every aspect of the analysis of multivariate data collected from a variety of fields including business and economics, health care, engineering, and environmental and physical sciences. High-Dimensional Covariance Estimation provides accessible and comprehensive coverage of the classical and modern approaches for estimating covariance matrices as well as their applications to the rapidly developing areas lying at the intersection of statistics and machine learning. Recently, the classical sample covariance methodologies have been modified and improved upon to meet the needs of statisticians and researchers dealing with large correlated datasets. High-Dimensional Covariance Estimation focuses on the methodologies based on shrinkage, thresholding, and penalized likelihood with applications to Gaussian graphical models, prediction, and mean-variance portfolio management. The book relies heavily on regression-based ideas and interpretations to connect and unify many existing methods and algorithms for the task. High-Dimensional Covariance Estimation features chapters on: Data, Sparsity, and RegularizationRegularizing the EigenstructureBanding, Tapering, and ThresholdingCovariance MatricesSparse Gaussian Graphical ModelsMultivariate Regression The book is an ideal resource for researchers in statistics, mathematics, business and economics, computer sciences, and engineering, as well as a useful text or supplement for graduate-level courses in multivariate analysis, covariance estimation, statistical learning, and high-dimensional data analysis.…

Condición: New. In English.

Hardback. Condición: New. New copy - Usually dispatched within 4 working days.

Condición: New. Methods for estimating sparse and large covariance matrices Covariance and correlation matrices play fundamental roles in every aspect of the analysis of multivariate data collected from a variety of fields including business and economics, health care, engineering, and environmental and physical sciences. Series: Wiley Series in Probability and Statistics. Num Pages: 208 pages, Illustrations. BIC Classification: PBT. Category: (P) Professional & Vocational. Dimension: 160 x 240 x 18. Weight in Grams: 470. . 2013. 1st Edition. Hardcover. . . . .…

Taschenbuch. Condición: Neu. CFD Simulation of Oilfield Separators | A Realistic Approach | Ali Pourahmadi Laleh (u. a.) | Taschenbuch | 252 S. | Englisch | 2012 | LAP LAMBERT Academic Publishing | EAN 9783845403519 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. …

Hardcover. Condición: Brand New. 1st edition. 208 pages. 9.30x6.30x0.70 inches. In Stock.

Condición: New. Methods for estimating sparse and large covariance matrices Covariance and correlation matrices play fundamental roles in every aspect of the analysis of multivariate data collected from a variety of fields including business and economics, health care, engineering, and environmental and physical sciences. Series: Wiley Series in Probability and Statistics. Num Pages: 208 pages, Illustrations. BIC Classification: PBT. Category: (P) Professional & Vocational. Dimension: 160 x 240 x 18. Weight in Grams: 470. . 2013. 1st Edition. Hardcover. . . . . Books ship from the US and Ireland.…

Gebunden. Condición: New. MOHSEN POURAHMADI, PhD, is Professor of Statistics at Texas A&M University. He is an elected member of the International Statistical Institute, a Fellow of the American Statistical Association, and a member of the American Mathematical Society. Dr. Pourahma.

Hardcover. Condición: Gut. Ex-library with stamp and library-signature. GOOD condition, some traces of use. Ancien Exemplaire de bibliothèque avec signature et cachet. BON état, quelques traces d'usure. Ehem. Bibliotheksexemplar mit Signatur und Stempel. GUTER Zustand, ein paar Gebrauchsspuren. 62 POU 9780471394341 Sprache: Englisch Gewicht in Gramm: 1150.…

Condición: New.

Buch. Condición: Neu. Neuware - Methods for estimating sparse and large covariance matrices Covariance and correlation matrices play fundamental roles in every aspect of the analysis of multivariate data collected from a variety of fields including business and economics, health care, engineering, and environmental and physical sciences. High-Dimensional Covariance Estimation provides accessible and comprehensive coverage of the classical and modern approaches for estimating covariance matrices as well as their applications to the rapidly developing areas lying at the intersection of statistics and machine learning. Recently, the classical sample covariance methodologies have been modified and improved upon to meet the needs of statisticians and researchers dealing with large correlated datasets. High-Dimensional Covariance Estimation focuses on the methodologies based on shrinkage, thresholding, and penalized likelihood with applications to Gaussian graphical models, prediction, and mean-variance portfolio management. The book relies heavily on regression-based ideas and interpretations to connect and unify many existing methods and algorithms for the task. High-Dimensional Covariance Estimation features chapters on: - Data, Sparsity, and Regularization - Regularizing the Eigenstructure - Banding, Tapering, and Thresholding - Covariance Matrices - Sparse Gaussian Graphical Models - Multivariate Regression The book is an ideal resource for researchers in statistics, mathematics, business and economics, computer sciences, and engineering, as well as a useful text or supplement for graduate-level courses in multivariate analysis, covariance estimation, statistical learning, and high-dimensional data analysis. …

gebundene Ausgabe. Condición: Gut. 414 Seiten Der Erhaltungszustand des hier angebotenen Werks ist trotz seiner Bibliotheksnutzung sehr sauber und kann entsprechende Merkmale aufweisen (Rückenschild, Instituts-Stempel.). In ENGLISCHER Sprache. Sprache: Englisch Gewicht in Gramm: 780.

HRD. Condición: New. New Book. Shipped from UK. Established seller since 2000.

Condición: New.

Condición: new.

Condición: New.

Hardback. Condición: New. Methods for estimating sparse and large covariance matrices Covariance and correlation matrices play fundamental roles in every aspect of the analysis of multivariate data collected from a variety of fields including business and economics, health care, engineering, and environmental and physical sciences. High-Dimensional Covariance Estimation provides accessible and comprehensive coverage of the classical and modern approaches for estimating covariance matrices as well as their applications to the rapidly developing areas lying at the intersection of statistics and machine learning. Recently, the classical sample covariance methodologies have been modified and improved upon to meet the needs of statisticians and researchers dealing with large correlated datasets. High-Dimensional Covariance Estimation focuses on the methodologies based on shrinkage, thresholding, and penalized likelihood with applications to Gaussian graphical models, prediction, and mean-variance portfolio management. The book relies heavily on regression-based ideas and interpretations to connect and unify many existing methods and algorithms for the task. High-Dimensional Covariance Estimation features chapters on: Data, Sparsity, and RegularizationRegularizing the EigenstructureBanding, Tapering, and ThresholdingCovariance MatricesSparse Gaussian Graphical ModelsMultivariate Regression The book is an ideal resource for researchers in statistics, mathematics, business and economics, computer sciences, and engineering, as well as a useful text or supplement for graduate-level courses in multivariate analysis, covariance estimation, statistical learning, and high-dimensional data analysis.…

Condición: As New. Unread book in perfect condition.