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Anglický jazyk
Estimation and Testing Under Sparsity
Autor: Sara van de Geer
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.... Viac o knihe
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O knihe
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.
- Vydavateľstvo: Springer International Publishing
- Rok vydania: 2016
- Formát: Paperback
- Rozmer: 235 x 155 mm
- Jazyk: Anglický jazyk
- ISBN: 9783319327730
