Estimation and Testing Under Sparsity École d'Été de Probabilités de Saint-Flour XLV – 2015 /
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 v...
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| Language: | English |
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Cham :
Springer International Publishing : Imprint: Springer,
2016.
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| Edition: | 1st ed. 2016. |
| Series: | École d'Été de Probabilités de Saint-Flour,
2159 |
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| Online Access: | https://doi.org/10.1007/978-3-319-32774-7 |
| Summary: | 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. |
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| Physical Description: | XIII, 274 p. online resource. |
| ISBN: | 9783319327747 |
| ISSN: | 0721-5363 ; |