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Statistical Learning with Sparsity: The Lasso and Generalizations
Trevor Hastie
€ 138.06
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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...
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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 moreReviews 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 each topic, the authors first give a concise...
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