Estimation and Testing under Sparsity École d'Été de Probabilités de Saint-Flour XLV - 2015 |
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Author:
| van de Geer, Sara |
Series title: | Lecture Notes in Mathematics Ser. |
ISBN: | 978-3-319-32774-7 |
Publication Date: | Jun 2016 |
Publisher: | Springer
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Book Format: | Ebook |
List Price: | USD $59.99 |
Book Description:
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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...
More DescriptionTaking 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.