Statistical learning with sparsity : the lasso and generalizations /

Discover New Methods for Dealing with High-Dimensional Data A 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 metho...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Hastie, Trevor (Autor), Tibshirani, Robert (Autor), Wainwright, Martin (Martin J.) (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Boca Raton : Chapman & Hall/CRC, 2015.
Edición:1st.
Colección:Chapman & Hall/CRC monographs on statistics & applied probability
Temas:
Mathematical statistics.
Least squares.
Linear models (Statistics)
Proof theory.
Théorie de la preuve.
MATHEMATICS / Algebra / Intermediate.
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Tabla de Contenidos:
  • 1. Introduction
  • 2. The lasso for linear models
  • 3. Generalized linear models
  • 4. Generalizations of the lasso penalty
  • 5. Optimization methods
  • 6. Statistical inference
  • 7. Matrix decompositions, approximations, and completion
  • 8. Sparse multivariate methods
  • 9. Graphs and model selection
  • 10. Signal approximation and compressed sensing
  • 11. Theoretical results for the lasso.

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