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[null,"1.","Record Nr.",null,"UNINA9910698305503321"]
[null,null,"Autore",null,"Ramsey David W"]
[null,null,"Titolo",null,"A new perspective on Mount St. Helens, dramatic landform change and associated hazards at the most active volcano in the Cascade Range [[electronic resource] /] / by David W. Ramsey ... [and others]"]
[null,null,"Pubbl/distr/stampa",null,"[Reston, Va.] : , : U.S. Dept. of the interior, U.S. Geological Survey, , 2008"]
[null,null,"Descrizione fisica",null,"4 remote-sensing images : digital, PDF file"]
[null,null,"Collana",null,"Scientific investigations map ; ; 3008"]
[null,null,"Soggetti",null,"Volcanic activity prediction - Washington (State) - Saint Helens, Mount"]
[{"$nulls":4},"Saint Helens, Mount (Wash.) Charts, diagrams, etc"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale cartografico a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Relief shown by shading."]
[{"$nulls":4},"Title from title screen (viewed on June 16, 2008)."]
[{"$nulls":4},"Includes text, 1 colored location map, 1 diagram, and 8 colored photos."]
[null,null,null,"Nota di bibliografia","Includes bibliographical references."]
[null,"2.","Record Nr.",null,"UNINA9910828712603321"]
[null,null,"Autore",null,"Pourahmadi Mohsen"]
[null,null,"Titolo",null,"High-dimensional covariance estimation / / Mohsen Pourahmadi"]
[null,null,"Pubbl/distr/stampa",null,"Hoboken, NJ, : Wiley, c2013"]
[null,null,"ISBN",null,"9781118573662"]
[{"$nulls":4},"1118573668"]
[{"$nulls":4},"9781118573617"]
[{"$nulls":4},"1118573617"]
[{"$nulls":4},"9781118573655"]
[{"$nulls":4},"111857365X"]
[null,null,"Edizione",null,"[1st ed.]"]
[null,null,"Descrizione fisica",null,"1 online resource (208 p.)"]
[null,null,"Collana",null,"Wiley series in probability and statistics"]
[null,null,"Classificazione",null,"MAT029020"]
[null,null,"Disciplina",null,"519.5/38"]
[null,null,"Soggetti",null,"Analysis of covariance"]
[{"$nulls":4},"Multivariate analysis"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Description based upon print version of record."]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"HIGH-DIMENSIONAL COVARIANCE ESTIMATION; CONTENTS; PREFACE; I MOTIVATION AND THE BASICS; 1 INTRODUCTION; 1.1 Least Squares and Regularized Regression; 1.2 Lasso: Survival of the Bigger; 1.3 Thresholding the Sample Covariance Matrix; 1.4 Sparse PCA and Regression; 1.5 Graphical Models: Nodewise Regression; 1.6 Cholesky Decomposition and Regression; 1.7 The Bigger Picture: Latent Factor Models; 1.8 Further Reading; 2 DATA, SPARSITY, AND REGULARIZATION; 2.1 Data Matrix: Examples; 2.2 Shrinking the Sample Covariance Matrix; 2.3 Distribution of the Sample Eigenvalues"]
[{"$nulls":4},"2.4 Regularizing Covariances Like a Mean2.5 The Lasso Regression; 2.6 Lasso: Variable Selection and Prediction; 2.7 Lasso: Degrees of Freedom and BIC; 2.8 Some Alternatives to the Lasso Penalty; 3 COVARIANCE MATRICES; 3.1 Definition and Basic Properties; 3.2 The Spectral Decomposition; 3.3 Structured Covariance Matrices; 3.4 Functions of a Covariance Matrix; 3.5 PCA: The Maximum Variance Property; 3.6 Modified Cholesky Decomposition; 3.7 Latent Factor Models; 3.8 GLM for Covariance Matrices; 3.9 GLM via the Cholesky Decomposition; 3.10 GLM for Incomplete Longitudinal Data"]
[{"$nulls":4},"3.10.1 The Incoherency Problem in Incomplete Longitudinal Data3.10.2 The Incomplete Data and The EM Algorithm; 3.11 A Data Example: Fruit Fly Mortality Rate; 3.12 Simulating Random Correlation Matrices; 3.13 "]
[{"$nulls":4},"Bayesian Analysis of Covariance Matrices; II COVARIANCE ESTIMATION: REGULARIZATION; 4 REGULARIZING THE EIGENSTRUCTURE; 4.1 Shrinking the Eigenvalues; 4.2 Regularizing The Eigenvectors; 4.3 A Duality between PCA and SVD; 4.4 Implementing Sparse PCA: A Data Example; 4.5 Sparse Singular Value Decomposition (SSVD); 4.6 Consistency of PCA; 4.7 Principal Subspace Estimation; 4.8 Further Reading"]
[{"$nulls":4},"5 SPARSE GAUSSIAN GRAPHICAL MODELS5.1 Covariance Selection Models: Two Examples; 5.2 Regression Interpretation of Entries of Σ-1; 5.3 Penalized Likelihood and Graphical Lasso; 5.4 Penalized Quasi-Likelihood Formulation; 5.5 Penalizing the Cholesky Factor; 5.6 Consistency and Sparsistency; 5.7 Joint Graphical Models; 5.8 Further Reading; 6 BANDING, TAPERING, AND THRESHOLDING; 6.1 Banding the Sample Covariance Matrix; 6.2 Tapering the Sample Covariance Matrix; 6.3 Thresholding the Sample Covariance Matrix; 6.4 Low-Rank Plus Sparse Covariance Matrices; 6.5 Further Reading"]
[{"$nulls":4},"7 MULTIVARIATE REGRESSION: ACCOUNTING FOR CORRELATION7.1 Multivariate Regression and LS Estimators; 7.2 Reduced Rank Regressions (RRR); 7.3 Regularized Estimation of B; 7.4 Joint Regularization of (B, Ω); 7.5 Implementing MRCE: Data Examples; 7.5.1 Intraday Electricity Prices; 7.5.2 Predicting Asset Returns; 7.6 Further Reading; BIBLIOGRAPHY; INDEX; WILEY SERIES IN PROBABILITY AND STATISTICS"]
[null,null,null,"Sommario/riassunto","\"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\"--"]
[{"$nulls":4},"\"The aim of this book is to provide computationally feasible and statistically efficient methods for estimating sparse and large covariance matrices of high-dimensional data\"--"]