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Statistics for High-Dimensional Data

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Cover of 'Statistics for High-Dimensional Data'

Table of Contents

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    Book Overview
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    Chapter 1 Introduction
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    Chapter 2 Lasso for linear models
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    Chapter 3 Generalized linear models and the Lasso
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    Chapter 4 The group Lasso
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    Chapter 5 Additive models and many smooth univariate functions
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    Chapter 6 Theory for the Lasso
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    Chapter 7 Variable selection with the Lasso
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    Chapter 8 Theory for ℓ1/ℓ2-penalty procedures
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    Chapter 9 Non-convex loss functions and ℓ1-regularization
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    Chapter 10 Stable solutions
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    Chapter 11 P-values for linear models and beyond
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    Chapter 12 Boosting and greedy algorithms
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    Chapter 13 Graphical modeling
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    Chapter 14 Probability and moment inequalities
Attention for Chapter 10: Stable solutions
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