ABSTRACT

Discover New Methods for Dealing with High-Dimensional DataA sparse statistical model has only a small number of nonzero parameters or weights; therefore, it is much easier to estimate and interpret than a dense model. Statistical Learning with Sparsity: The Lasso and Generalizations presents methods that exploit sparsity to help recover the underl

chapter 1|6 pages

Introduction

chapter 2|22 pages

The Lasso for Linear Models

chapter 3|26 pages

Generalized Linear Models

chapter 4|40 pages

Generalizations of the Lasso Penalty

chapter 5|44 pages

Optimization Methods

chapter 6|28 pages

Statistical Inference

chapter 8|40 pages

Sparse Multivariate Methods

chapter 9|28 pages

Graphs and Model Selection

chapter 10|20 pages

Signal Approximation and Compressed Sensing

chapter 11|26 pages

Theoretical Results for the Lasso