Estimation and Testing Under Sparsity
Author | : Sara van de Geer |
Publisher | : Springer |
Total Pages | : 278 |
Release | : 2016-06-28 |
Genre | : Mathematics |
ISBN | : 3319327747 |
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Taking the Lasso method as its starting point, this book describes the main ingredients needed to study general loss functions and sparsity-inducing regularizers. It also provides a semi-parametric approach to establishing confidence intervals and tests. Sparsity-inducing methods have proven to be very useful in the analysis of high-dimensional data. Examples include the Lasso and group Lasso methods, and the least squares method with other norm-penalties, such as the nuclear norm. The illustrations provided include generalized linear models, density estimation, matrix completion and sparse principal components. Each chapter ends with a problem section. The book can be used as a textbook for a graduate or PhD course.