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  • [Trevor Hastie]Statistical Learning with Sparsity The Lasso and Generalizations(pdf){Zzzzz}
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  • Statistical Learning Sparsity
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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 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 linear models and support vector machines, cover generalized penalties such as the elastic net and group lasso, and review numerical methods for optimization. They also present statistical inference methods for fitted (lasso) models, including the bootstrap, Bayesian methods, and recently developed approaches. In addition, the book examines matrix decomposition, sparse multivariate analysis, graphical models, and compressed sensing. It concludes with a survey of theoretical results for the lasso.

In this age of big data, the number of features measured on a person or object can be large and might be larger than the number of observations. This book shows how the sparsity assumption allows us to tackle these problems and extract useful and reproducible patterns from big datasets. Data analysts, computer scientists, and theorists will appreciate this thorough and up-to-date treatment of sparse statistical modeling.

[b]Editorial Reviews
About the Author[/b]

Trevor Hastie is the John A. Overdeck professor of mathematical sciences, professor of statistics, and professor of health research and policy at Stanford University. His research focuses on applied problems in biology, genomics, medicine, and industry, with an emphasis on statistical; models, algorithms, and software.

Robert Tibshirani is a professor of health research and policy and professor of statistics at Stanford University. He develops and studies statistical and computational tools for problems in biology, genomics, medicine, and industry.

Martin Wainwright is a professor in the Department of Electrical Engineering and Computer Sciences and the Department of Statistics at the University of California, Berkeley. His research focuses on high-dimensional statistics, graphical models and machine learning, statistics and privacy, nonparametric statistics, and distributed algorithms and optimization.

[b][color=blue]Publisher: Chapman and Hall/CRC (June 18, 2015)
Language: English
ISBN-10: 1498712169
ISBN-13: 978-1498712163[/color][/b]




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