Statistical learning with sparsity: the lasso and generalizations (Record no. 203397)

000 -LEADER
fixed length control field 04756 a2200241 4500
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 160614b2015 xxu||||| |||| 00| 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781498712163
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 519.5
Item number H2S8
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Hastie, Trevor
9 (RLIN) 333529
245 ## - TITLE STATEMENT
Title Statistical learning with sparsity: the lasso and generalizations
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Place of publication, distribution, etc Boca Raton
Name of publisher, distributor, etc CRC Press
Date of publication, distribution, etc 2015
300 ## - PHYSICAL DESCRIPTION
Extent xiii, 351 p.
440 ## - SERIES STATEMENT/ADDED ENTRY--TITLE
Title Chapman & Hall/CRC Monographs on Statistics & Applied Probability
9 (RLIN) 333530
504 ## - BIBLIOGRAPHY, ETC. NOTE
Bibliography, etc Table of Contents:<br/><br/>1. Introduction<br/><br/>2. The Lasso for Linear Models <br/>Introduction <br/>The Lasso Estimator <br/>Cross-Validation and Inference <br/>Computation of the Lasso Solution <br/>Degrees of Freedom <br/>Uniqueness of the Lasso Solutions <br/>A Glimpse at the Theory <br/>The Nonnegative Garrote <br/>ℓq Penalties and Bayes Estimates <br/>Some Perspective <br/><br/>3. Generalized Linear Models <br/>Introduction <br/>Logistic Regression <br/>Multiclass Logistic Regression <br/>Log-Linear Models and the Poisson GLM <br/>Cox Proportional Hazards Models <br/>Support Vector Machines <br/>Computational Details and glmnet <br/><br/>4. Generalizations of the Lasso Penalty<br/>Introduction<br/>The Elastic Net <br/>The Group Lasso<br/>Sparse Additive Models and the Group Lasso<br/>The Fused Lasso <br/>Nonconvex Penalties<br/><br/>5. Optimization Methods <br/>Introduction <br/>Convex Optimality Conditions <br/>Gradient Descent<br/>Coordinate Descent <br/>A Simulation Study <br/>Least Angle Regression <br/>Alternating Direction Method of Multipliers <br/>Minorization-Maximization Algorithms <br/>Biconvexity and Alternating Minimization <br/>Screening Rules<br/> <br/>6. Statistical Inference <br/>The Bayesian Lasso <br/>The Bootstrap <br/>Post-Selection Inference for the Lasso <br/>Inference via a Debiased Lasso <br/>Other Proposals for Post-Selection Inference <br/><br/>7. Matrix Decompositions, Approximations, and Completion <br/>Introduction <br/>The Singular Value Decomposition<br/>Missing Data and Matrix Completion <br/>Reduced-Rank Regression <br/>A General Matrix Regression Framework <br/>Penalized Matrix Decomposition <br/>Additive Matrix Decomposition<br/> <br/>8. Sparse Multivariate Methods <br/>Introduction <br/>Sparse Principal Components Analysis <br/>Sparse Canonical Correlation Analysis <br/>Sparse Linear Discriminant Analysis <br/>Sparse Clustering <br/><br/>9. Graphs and Model Selection <br/>Introduction <br/>Basics of Graphical Models<br/>Graph Selection via Penalized Likelihood <br/>Graph Selection via Conditional Inference <br/>Graphical Models with Hidden Variables<br/> <br/>10. Signal Approximation and Compressed Sensing <br/>Introduction <br/>Signals and Sparse Representations <br/>Random Projection and Approximation <br/>Equivalence between ℓ0 and ℓ1 Recovery<br/> <br/>11. Theoretical Results for the Lasso <br/>Introduction <br/>Bounds on Lasso ℓ2-error <br/>Bounds on Prediction Error <br/>Support Recovery in Linear Regression <br/>Beyond the Basic Lasso <br/><br/>
520 ## - SUMMARY, ETC.
Summary, etc Discover New Methods for Dealing with High-Dimensional Data<br/><br/>A sparse statistical model has only a small number of nonzero parameters or weights; therefore, it is much easier to estimate and interpret than a dense model. Statistical Learning with Sparsity: The Lasso and Generalizations presents methods that exploit sparsity to help recover the underlying signal in a set of data.<br/><br/>Top experts in this rapidly evolving field, the authors describe the lasso for linear regression and a simple coordinate descent algorithm for its computation. They discuss the application of l1 penalties to generalized linear models and support vector machines, cover generalized penalties such as the elastic net and group lasso, and review numerical methods for optimization. They also present statistical inference methods for fitted (lasso) models, including the bootstrap, Bayesian methods, and recently developed approaches. In addition, the book examines matrix decomposition, sparse multivariate analysis, graphical models, and compressed sensing. It concludes with a survey of theoretical results for the lasso.<br/><br/>In this age of big data, the number of features measured on a person or object can be large and might be larger than the number of observations. This book shows how the sparsity assumption allows us to tackle these problems and extract useful and reproducible patterns from big datasets. Data analysts, computer scientists, and theorists will appreciate this thorough and up-to-date treatment of sparse statistical modeling.<br/><br/>(https://www.crcpress.com/Statistical-Learning-with-Sparsity-The-Lasso-and-Generalizations/Hastie-Tibshirani-Wainwright/p/book/9781498712163)
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Mathematical statistics
9 (RLIN) 6413
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Least squares
9 (RLIN) 67820
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Linear models (Statistics)
9 (RLIN) 43688
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Proof theory
9 (RLIN) 139265
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name Tibshirani, Robert
9 (RLIN) 333531
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name Wainwright, Martin
9 (RLIN) 333532
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme
Item type Books
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Collection code Permanent location Current location Shelving location Date acquired Source of acquisition Cost, normal purchase price Total Checkouts Total Renewals Full call number Barcode Date last seen Date last borrowed Cost, replacement price Koha item type
          Non-fiction Vikram Sarabhai Library Vikram Sarabhai Library General Stacks 15/06/2016 13 4773.73 3 5 519.5 H2S8 192275 30/04/2019 02/09/2018 5967.17 Books

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