Statistical learning with sparsity : the lasso and generalizations

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...

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Detalles Bibliográficos
Otros Autores: Hastie, Trevor, author (author), Tibshirani, Robert, author, Wainwright, Martin (Martin J.), author
Formato: Libro electrónico
Idioma:Inglés
Publicado: Boca Raton : CRC Press [2015]
Edición:1st
Colección:Monographs on statistics and applied probability (Series) ; 143.
Materias:
Mathematical statistics.
Least squares.
Linear models (Statistics)
Proof theory.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009755102006719
Descripción
Sumario: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 methods that exploit sparsity to help recover the underlying signal in a set of data.Top experts in this rapidly evolving field, the authors describe the lasso for linear regression and a simple coordinate descent algorithm for its computation. They discuss the application of ℓ1 penalties to generalized l
Notas:Description based upon print version of record.
Descripción Física:1 online resource (354 p.)
Bibliografía:Includes bibliographical references.
ISBN:9780429171581

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