9781118034293 - High-Dimensional Covariance Estimation: With High-Dimensional Data (Wiley Series in Probability and Statistics) von Pourahmadi, Mohsen (8 Ergebnisse)

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

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

Zustand: New. In.

Zustand: New. pp. 208.

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

Zustand: 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. Zustand: 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.

Buch. Zustand: 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.