Kooperativer Bibliotheksverbund

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Umfang: 1 online resource (x, 184 pages) : , illustrations

ISBN: 9781118573617 , 1118573617 , 9781118573655 , 111857365X , 9781118573662 , 1118573668 , 1118034295 , 9781118034293

Serie: Wiley series in probability and statistics

Inhalt: "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."--Publisher's website.

Anmerkung: Motivation And The Basics -- , Introduction -- , Least Squares and Regularized Regression -- , Lasso: Survival of the Bigger -- , Thresholding the Sample Covariance Matrix -- , Sparse PCA and Regression -- , Graphical Models: Nodewise Regression -- , Cholesky Decomposition and Regression -- , Bigger Picture: Latent Factor Models -- , Further Reading -- , Data, Sparsity, And Regularization -- , Data Matrix: Examples -- , Shrinking the Sample Covariance Matrix -- , Distribution of the Sample Eigenvalues -- , Regularizing Covariances Like a Mean -- , Lasso Regression -- , Lasso: Variable Selection and Prediction -- , Lasso: Degrees of Freedom and BIC -- , Some Alternatives to the Lasso Penalty -- , Covariance Matrices -- , Definition and Basic Properties -- , Spectral Decomposition -- , Structured Covariance Matrices -- , Functions of a Covariance Matrix -- , PCA: The Maximum Variance Property -- , Modified Cholesky Decomposition -- , Latent Factor Models -- , GLM for Covariance Matrices -- , GLM via the Cholesky Decomposition -- , GLM for Incomplete Longitudinal Data -- , Incoherency Problem in Incomplete Longitudinal Data -- , Incomplete Data and The EM Algorithm -- , Data Example: Fruit Fly Mortality Rate -- , Simulating Random Correlation Matrices -- , Bayesian Analysis of Covariance Matrices -- , Covariance Estimation: Regularization -- , Regularizing The Eigenstructure -- , Shrinking the Eigenvalues -- , Regularizing The Eigenvectors -- , Duality between PCA and SVD -- , Implementing Sparse PCA: A Data Example -- , Sparse Singular Value Decomposition (SSVD) -- , Consistency of PCA -- , Principal Subspace Estimation -- , Further Reading -- , Sparse Gaussian Graphical Models -- , Covariance Selection Models: Two Examples -- , Regression Interpretation of Entries of ?-1 -- , Penalized Likelihood and Graphical Lasso -- , Penalized Quasi-Likelihood Formulation -- , Penalizing the Cholesky Factor -- , Consistency and Sparsistency -- , Joint Graphical Models -- , Further Reading -- , Banding, Tapering, And Thresholding -- , Banding the Sample Covariance Matrix -- , Tapering the Sample Covariance Matrix -- , Thresholding the Sample Covariance Matrix -- , Low-Rank Plus Sparse Covariance Matrices -- , Further Reading -- , Multivariate Regression: Accounting For Correlation -- , Multivariate Regression and LS Estimators -- , Reduced Rank Regressions (RRR) -- , Regularized Estimation of B -- , Joint Regularization of (B, ?) -- , Implementing MRCE: Data Examples -- , Intraday Electricity Prices -- , Predicting Asset Returns -- , Further Reading.

Weitere Ausg.: Print version: Pourahmadi, Mohsen. High-dimensional covariance estimation. Hoboken, NJ : Wiley, ©2013 ISBN 9781118034293

Sprache: Englisch

Schlagwort(e): Electronic books. ; Libros electronicos. ; Electronic books.

DOI: 10.1002/9781118573617

URL: https://onlinelibrary.wiley.com/doi/book/10.1002/9781118573617

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Umfang: x, 184 Seiten , Illustrationen , 25 cm

ISBN: 9781118034293

Serie: Wiley series in probability and statistics

Inhalt: "Focusing on methodology and computation more than on theorems and proofs, this book provides computationally feasible and statistically efficient methods for estimating sparse and large covariance matrices of high-dimensional data. Extensive in breadth and scope, it features ample applications to a number of applied areas, including business and economics, computer science, engineering, and financial mathematics; recognizes the important and significant contributions of longitudinal and spatial data; and includes various computer codes in R throughout the text and on an author-maintained web site"..

Anmerkung: Includes bibliographical references (pages 171-179) and index

Weitere Ausg.: Erscheint auch als Online-Ausgabe ISBN 978-1-118-57361-7

Sprache: Englisch

Fachgebiete: Wirtschaftswissenschaften

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Umfang: x, 184 p. : , ill.

Ausgabe: Electronic reproduction. Ann Arbor, MI : ProQuest, 2015. Available via World Wide Web. Access may be limited to ProQuest affiliated libraries.

Serie: Wiley series in probability and statistics

Sprache: Englisch

Schlagwort(e): Electronic books.

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