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Statistical learning with sparsity : the lasso and generalizations / Trevor Hastie, Stanford University, USA; Robert Tibshirani, Stanford University, USA; Martin Wainwright, University of California, Berkeley, USA.
- Format:
- Book
- Author/Creator:
- Hastie, Trevor, author.
- Tibshirani, Robert, author.
- Wainwright, Martin (Martin J.), author.
- Series:
- Monographs on statistics and applied probability (Series) ; 143.
- Monographs on Statistics and Applied Probability ; 143
- Language:
- English
- Subjects (All):
- Mathematical statistics.
- Least squares.
- Linear models (Statistics).
- Proof theory.
- Physical Description:
- 1 online resource (354 p.)
- Edition:
- 1st.
- Place of Publication:
- Boca Raton : CRC Press, [2015]
- Language Note:
- English
- Summary:
- 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
- Contents:
- 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
- Notes:
- Description based upon print version of record.
- Includes bibliographical references.
- Description based on print version record.
- Description based on publisher supplied metadata and other sources.
- ISBN:
- 9781040074176
- 1040074170
- 9780429171581
- 0429171587
- OCLC:
- 908931926
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