Statistical learning with sparsity : the lasso and generalizations /: the lasso and generalizations. (2015)

• Record Type: Book
• Further Information: Note: Trevor Hastie, Rob Tibshirani, Martin Wainwright.
• Authors: Hastie, Trevor; Tibshirani, Robert; Wainwright, Martin (Martin J.)
• Contents: Introduction The Lasso for Linear Models; Introduction; The Lasso Estimator; Cross-Validation and Inference; Computation of the Lasso Solution; Degrees of Freedom; Uniqueness of the Lasso Solutions; A Glimpse at the Theory; The Nonnegative Garrote; ℓq Penalties and Bayes Estimates; Some Perspective Generalized Linear Models; Introduction; Logistic Regression; Multiclass Logistic Regression; Log-Linear Models and the Poisson GLM; Cox Proportional Hazards Models; Support Vector Machines; Computational Details and glmnet Generalizations of the Lasso Penalty; Introduction; The Elastic Net; The Group Lasso; Sparse Additive Models and the Group Lasso; The Fused Lasso; Nonconvex Penalties Optimization Methods; Introduction; Convex Optimality Conditions; Gradient Descent; Coordinate Descent ; A Simulation Study; Least Angle Regression; Alternating Direction Method of Multipliers; Minorization-Maximization Algorithms; Biconvexity and Alternating Minimization; Screening Rules Statistical Inference; The Bayesian Lasso; The Bootstrap; Post-Selection Inference for the Lasso; Inference via a Debiased Lasso; Other Proposals for Post-Selection Inference Matrix Decompositions, Approximations, and Completion; Introduction; The Singular Value Decomposition; Missing Data and Matrix Completion; Reduced-Rank Regression; A General Matrix Regression Framework; Penalized Matrix Decomposition; Additive Matrix Decomposition Sparse Multivariate Methods; Introduction; Sparse Principal Components Analysis; Sparse Canonical Correlation Analysis; Sparse Linear Discriminant Analysis; Sparse Clustering Graphs and Model Selection; Introduction; Basics of Graphical Models; Graph Selection via Penalized Likelihood; Graph Selection via Conditional Inference; Graphical Models with Hidden Variables Signal Approximation and Compressed Sensing; Introduction; Signals and Sparse Representations; Random Projection and Approximation; Equivalence between ℓ0 and ℓ1 Recovery Theoretical Results for the Lasso; Introduction ; Bounds on Lasso ℓ2 -error; Bounds on Prediction Error; Support Recovery in Linear Regression; Beyond the Basic Lasso Bibliography Author Index Index Bibliographic Notes and Exercises appear at the end of each chapter.
• Edition: 1st
• Publisher Details: Boca Raton : Chapman & Hall/CRC
• Publication Date: 2015
• Extent: 1 online resource, illustrations (colour)
• Subjects: 519.5; Mathematical statistics; Least squares; Linear models (Statistics); Proof theory
• Languages: English
• ISBNs: 9781498712170
• Related ISBNs: 9781498712163
• Notes: Note: Includes bibliographical references and index.; Note: Description based on CIP data; item not viewed.
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.