Statistical learning with sparsity the lasso and generalizations
Discover New Methods for Dealing with High-Dimensional DataA sparse statistical model has only a small number of nonzero parameters or weights; therefore, it is much easier to estimate and interpret than a dense model. Statistical Learning with Sparsity: The Lasso and Generalizations presents method...
| Otros Autores: | , , |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Boca Raton :
CRC Press
[2015]
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| Edición: | 1st |
| Colección: | Monographs on statistics and applied probability (Series) ;
143. |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009755102006719 |
Tabla de Contenidos:
- Front Cover; Contents; Preface; Chapter 1: Introduction; Chapter 2: The Lasso for Linear Models; Chapter 3: Generalized Linear Models; Chapter 4: Generalizations of the Lasso Penalty; Chapter 5: Optimization Methods; Chapter 6: Statistical Inference; Chapter 7: Matrix Decompositions, Approximations, and Completion; Chapter 8: Sparse Multivariate Methods; Chapter 9: Graphs and Model Selection; Chapter 10: Signal Approximation and Compressed Sensing; Chapter 11: Theoretical Results for the Lasso; Bibliography; Back Cover
