全新正版稀疏统计学习(英文版)9787519261870世界图书出版公司

Preface
1 Introduction
2 The Lasso for Linear Models
2.1 Introduction
2.2 The Lasso Estimator
. Cross-Vlalidation and Inference
2.4 Computation of the Lasso Solution
2.4.1 Single Predictor：Soft Thresholding
2.4.2 Multiple Predictors：Cyclic Coordinate Descent
2.4.3 Soft-Thresholding and Orthogonal Bases
2.5 Degrees of Freedom
2.6 Uniqueness of the LaSSO Solutions
2.7 A Glimpse at the Theory
2.8 The Nonnegative Garrote
2.9 Penalties and Bayes Estimates
2.10 Some Perspective
Exercises
3 Generalized Linear M0dels
3.1 Introduction
3.2 Logistic Regression
3.2.1 Example：Document Classification
3.2.2 Algorithms
3.3 Multiclass Logistic Regression
3.3.1 Example：Handwritten Digits
3.3.2 Algorithms
3.3.3 Grouped。Lasso Multinomial
3.4 Log-Linear Models and the Poisson GLM
3.4.1 Example：Distribution Smoothing
3.5 COX Proportional Hazards Models
3.5.1 Cross-Validation
3.5.2 Pre-Validation
3.6 Support Vector Machines
3.6.1 Logistic Regression with Separable Data
3.7 Computational Details and glmnet
Bibliographic Notes
Exercises
4 Generalizations of the Lasso Penalty
4.1 Introduction
4.2 The Elastic Net
4.3 The Group Lasso
4.3.1 Computation for the Group Lasso
4.3.2 Sparse Group Lasso
4.3.3 The Overlap Group Lasso
4.4 Sparse Additive Models and the Group Lasso
4.4.1 Additive Models and Backfitting
4.4.2 Sparse Additive Models and Backfitting
4.4.3 Approaches Using Optimization and the Group Lasso
4.4.4 Multiple Penalization for Sparse Additive Models
4.5 The Fused Lasso
4.5.1 Fitting the Fused Lasso
4.5.1.1 Reparametrization
4.5.1.2 A Path Algorithm
4.5.1.3 A Dual Path Algorithm
4.5.1.4 Dynamic Programming for the Fused Lass0
4.5.2 Trend Filtering
4.5.3 Nearly Isotonic Regression
4.6 Nonconvex Penalties
Bibliographic Notes
Exercises
5 Optimization Methods
5.1 Introduction
5.2 Convex Optimality Conditions
5.2.1 Optimality for Differentiable Problems
5.2.2 Nondifferentiable Functions and Subgradients
5.3 Gradient Descent
5.3.1 Unconstrained Gradient Descent
5.3.2 Projected Gradient Methods
5.3.3 ProximaI Gradient Methods
5.3.4 Accelerated Gradient Methods
5.4 Coordinate Descent
5.4.1 SeparabiliyndCordinate Descent
5.4.2 Linear Regression and the Lasso
5.4.3 Logistic Regression and Generalized Linear Models
5.5 A Simulation Study
5.6 Least Angle Regression
5.7 Alternating Direction Method of Multipliers
5.8 Minorization-Maximization Algorithms
5.9 Biconvexity and Alternating Minimazation
5.10 Screening Rules
Bibliographic Notes
Appendix
Exercises
6 Statistical Inference
6.1 The Bayesian Lasso
6.2 The Bootstrap
6.3 Post.Selection Inference for the Lasso
6.3.I The Covariance Test
6.3.2 A General Scheme for Post-Selection Inference
6.3.2.1 Fixed-r Inference for the Lasso
6.3.2.2 The Spacing Test for LAR
6.3.3 What Hypothesis Is Being Tested?
6.3.4 Back to Forward Stepwise Regression
6.4 Inference via a Debiased Lasso
6.5 Other Proposals for Post-Selection Inference
Bibliographic Notes
Exercises
7 Matrix Decoitions，Approximations，and Comlio
7.1 Introduction
7.2 The Singular Value Decoition
7.3 Missing Data and Matrix Comlio
7.3.1 The Netflix Movie Challenge
7.3.2 Matrix Comlio Using Nuclear Norm
7.3.3 Theoretical Results for Matrix Comlio
7.3.4 Maximum Margin Factorization and Related Methods
7.4 Reduced-Rank Regression
7.5 A General Matrix Regression Framework
7.6 Penalized Matrix Decoition
7.7 Additive Matrix Decoition
Bibliographic Notes
Exercises
8 Sparse Multivariate Methods
8.1 Introduction
8.2 Sparse Principal Components

Trevor Hastie 美国统计学家和计算机科学家，斯坦福大学统计学教授，英国统计学会、靠前数理统计协会和美国统计学会会士。Hastie参与开发了R中的大部分统计建模软件和环境，发明了主曲线和主曲面。