Statistics For Highdimensional Data Methodstheory And Applications 2016th Edition Peter Buhlmannsara Van De Geer by Peter Buhlmann·sara Van De Geer instant download after payment.

1 (p1): 1 Introduction
1 (p1-1): 1.1 The framework
2 (p1-2): 1.2 The possibilities and challenges
3 (p1-3): 1.3 About the book
3 (p1-3-1): 1.3.1 Organization of the book
4 (p1-4): 1.4 Some examples
5 (p1-4-1): 1.4.1 Prediction and biomarker discovery in genomics
7 (p2): 2 Lasso for linear models
7 (p2-1): 2.1 Organization of the chapter
8 (p2-2): 2.2 Introduction and preliminaries
9 (p2-2-1): 2.2.1 The Lasso estimator
10 (p2-3): 2.3 Orthonormaldesign
11 (p2-4): 2.4 Prediction
12 (p2-4-1): 2.4.1 Practical aspects about the Lasso for prediction
13 (p2-4-2): 2.4.2 Some results from asymptotic theory
14 (p2-5): 2.5 Variable screening and||^β-β0||q-norms
17 (p2-5-1): 2.5.1 Tuning parameter selection for variable screening
18 (p2-5-2): 2.5.2 Motif regression for DNA binding sites
19 (p2-6): 2.6 Variable selection
22 (p2-6-1): 2.6.1 Neighborhood stability and irrepresentable condition
23 (p2-7): 2.7 Key properties and corresponding assumptions：a summary
25 (p2-8): 2.8 The adaptive Lasso：a two-stage procedure
25 (p2-8-1): 2.8.1 An illustration：simulated data and motif regression
27 (p2-8-2): 2.8.2 Orthonormal design
28 (p2-8-3): 2.8.3 The adaptive Lasso：variable selection under weak conditions
29 (p2-8-4): 2.8.4 Computation
30 (p2-8-5): 2.8.5 Multi-step adaptive Lasso
32 (p2-8-6): 2.8.6 Non-convex penalty functions
33 (p2-9): 2.9 Thresholdingthe Lasso
34 (p2-10): 2.10 The relaxed Lasso
34 (p2-11): 2.11 Degrees of freedom of the Lasso
36 (p2-12): 2.12 Path-following algorithms
38 (p2-12-1): 2.12.1 Coordinatewise optimization and shooting algorithms
41 (p2-13): 2.13 Elastic net：an extension
42 (p2-14): Problems
45 (p3): 3 Generalized linear models and the Lasso
45 (p3-1): 3.1 Organization of the chapter
45 (p3-2): 3.2 Introduction and preliminaries
46 (p3-2-1): 3.2.1 The Lasso estimator：penalizing the negative log-likelihood
47 (p3-3): 3.3 Important examples of generalized linear models
47 (p3-3-1): 3.3.1 Binary response variable and logistic regression
49 (p3-3-2): 3.3.2…