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Estimation and Testing Under Sparsity : Ecole d'Ete de Probabilites de Saint-Flour XLV - 2015. 1st ed. 2016.
・ISBN 978-3-319-32773-0 paper EUR 54.99
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| 著者・編者 | van de Geer, Sara, |
|---|---|
| シリーズ | Lecture Notes in Mathematics |
| 出版社 | (Springer International Publishing AG, SZ) |
| 出版年月 | 2016 |
| ページ数 | 274 pp. |
| 言語 | ENG |
| ニュース番号 | ＜M25-24235＞ |
解説
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.