Estimation and Testing Under Sparsity
École d'Été de Probabilités de Saint-Flour XLV – 2015
Nonfiction, Science & Nature, Mathematics, Statistics, Computers, Application Software
| Author: | Sara van de Geer | ISBN: | 9783319327747 |
| Publisher: | Springer International Publishing | Publication: | June 28, 2016 |
| Imprint: | Springer | Language: | English |
| Author: | Sara van de Geer |
| ISBN: | 9783319327747 |
| Publisher: | Springer International Publishing |
| Publication: | June 28, 2016 |
| Imprint: | Springer |
| Language: | English |
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.
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.
