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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| Format: | eBook |
| Language: | English |
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Cham
Springer International Publishing
2016, 2016
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| Edition: | 1st ed. 2016 |
| Series: | École d'Été de Probabilités de Saint-Flour
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| Collection: | Springer eBooks 2005- - Collection details see MPG.ReNa |
| 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 |