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Estimation and Testing Under Sparsity

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Cover of 'Estimation and Testing Under Sparsity'

Table of Contents

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    Book Overview
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    Chapter 1 Introduction
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    Chapter 2 The Lasso
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    Chapter 3 The Square-Root Lasso
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    Chapter 4 The Bias of the Lasso and Worst Possible Sub-directions
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    Chapter 5 Confidence Intervals Using the Lasso
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    Chapter 6 Structured Sparsity
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    Chapter 7 General Loss with Norm-Penalty
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    Chapter 8 Empirical Process Theory for Dual Norms
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    Chapter 9 Probability Inequalities for Matrices
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    Chapter 10 Inequalities for the Centred Empirical Risk and Its Derivative
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    Chapter 11 The Margin Condition
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    Chapter 12 Some Worked-Out Examples
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    Chapter 13 Brouwer’s Fixed Point Theorem and Sparsity
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    Chapter 14 Asymptotically Linear Estimators of the Precision Matrix
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    Chapter 15 Lower Bounds for Sparse Quadratic Forms
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    Chapter 16 Symmetrization, Contraction and Concentration
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    Chapter 17 Chaining Including Concentration
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    Chapter 18 Metric Structure of Convex Hulls
Attention for Chapter 11: The Margin Condition
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