 Hastie, Trevor
 Boca Raton : CRC Press, Taylor & Francis Group, [2015]
 Description
 Book — xv, 351 pages : illustrations (some color) ; 25 cm.
 Summary

 Introduction
 The Lasso for Linear Models Introduction The Lasso Estimator CrossValidation and Inference Computation of the Lasso Solution Degrees of Freedom Uniqueness of the Lasso Solutions A Glimpse at the Theory The Nonnegative Garrote q Penalties and Bayes Estimates Some Perspective
 Generalized Linear Models Introduction Logistic Regression Multiclass Logistic Regression LogLinear 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 MinorizationMaximization Algorithms Biconvexity and Alternating Minimization Screening Rules
 Statistical Inference The Bayesian Lasso The Bootstrap PostSelection Inference for the Lasso Inference via a Debiased Lasso Other Proposals for PostSelection Inference
 Matrix Decompositions, Approximations, and Completion Introduction The Singular Value Decomposition Missing Data and Matrix Completion ReducedRank 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 0 and 1 Recovery
 Theoretical Results for the Lasso Introduction Bounds on Lasso 2error 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.
 (source: Nielsen Book Data)
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