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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Bibliographic Details
Call Number:Libro Electrónico
Main Authors: Hastie, Trevor (Author), Tibshirani, Robert (Author), Wainwright, Martin (Martin J.) (Author)
Format: Electronic eBook
Language:Inglés
Published: Boca Raton : Chapman & Hall/CRC, 2015.
Edition:1st.
Series:Chapman & Hall/CRC monographs on statistics & applied probability
Subjects:
Mathematical statistics.
Least squares.
Linear models (Statistics)
Proof theory.
Théorie de la preuve.
MATHEMATICS / Algebra / Intermediate.
Online Access:Texto completo (Requiere registro previo con correo institucional)
Table of Contents:
  • 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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