Statistical Learning with Sparsity: The Lasso and Generalizations
Trevor Hastie
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Description for Statistical Learning with Sparsity: The Lasso and Generalizations
Hardback. Series: Chapman & Hall/CRC Monographs on Statistics & Applied Probability. Num Pages: 367 pages, 99 colour illustrations, 11 colour tables. BIC Classification: PBT; TJFM. Category: (G) General (US: Trade). Dimension: 244 x 163 x 22. Weight in Grams: 770.
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 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 ... Read more
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 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 ... Read more
Product Details
Publisher
Taylor & Francis Inc
Format
Hardback
Publication date
2015
Series
Chapman & Hall/CRC Monographs on Statistics & Applied Probability
Condition
New
Weight
805g
Number of Pages
367
Place of Publication
Portland, United States
ISBN
9781498712163
SKU
V9781498712163
Shipping Time
Usually ships in 4 to 8 working days
Ref
99-6
About Trevor Hastie
Trevor Hastie is the John A. Overdeck Professor of Statistics at Stanford University. Prior to joining Stanford University, Professor Hastie worked at AT&T Bell Laboratories, where he helped develop the statistical modeling environment popular in the R computing system. Professor Hastie is known for his research in applied statistics, particularly in the fields of data mining, bioinformatics, and machine learning. ... Read more
Reviews for Statistical Learning with Sparsity: The Lasso and Generalizations
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 The book includes all the major branches of statistical learning. For ... Read more