Statistics for high-dimensional data : methods, theory and applications / by Peter Bühlmann, Sara van de Geer
Material type:![Text](/opac-tmpl/lib/famfamfam/BK.png)
- 9783642201912 (hdbk. : acidfree paper)
- 3642201911 (hdbk. : acidfree paper)
- 519.5 BUH
Item type | Current library | Home library | Call number | Status | Date due | Barcode | |
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Dept. of Statistics General Stacks | Dept. of Statistics | 519.5 BUH (Browse shelf(Opens below)) | Available | STA10147 |
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Includes bibliographical references (p. 547-556) and indexes.
Introduction -- Lasso for linear models -- Generalized linear models and the Lasso -- The group Lasso --Additive models and many smooth univariate functions -- Theory for the Lasso -- Variable selection with the Lasso -- Theory for l₁/l₂-penalty procedures -- Non-convex loss functions and l₁-regulation -- Stable solutions -- P-values for linear models and beyond -- Boosting and greedy algorithms -- Graphical modeling -- Probabililty and moment inequalities.
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