Description
Book SynopsisDiscover 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 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 â1 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 d
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"The authors study and analyze methods using the sparsity property of some statistical models in order to recover the underlying signal in a dataset. They focus on the Lasso technique as an alternative to the standard least-squares method."
—Zentralblatt MATH 1319
Table of ContentsIntroduction. The Lasso for Linear Models. Generalized Linear Models. Generalizations of the Lasso Penalty. Optimization Methods. Statistical Inference. Matrix Decompositions, Approximations, and Completion. Sparse Multivariate Methods. Graphs and Model Selection. Signal Approximation and Compressed Sensing. Theoretical Results for the Lasso. Bibliography. Author Index. Index.
