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Regularization and variable selection via the elastic net
Summary. We propose the elastic net, a new regularization and variable selection method. Real world data and a simulation study show that the elastic net often outperforms the lasso, while enjoying aExpand
Least angle regression
The purpose of model selection algorithms such as All Subsets, Forward Selection and Backward Elimination is to choose a linear model on the basis of the same set of data to which the model will beExpand
The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition
This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression and path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. Expand
Regularization Paths for Generalized Linear Models via Coordinate Descent.
In comparative timings, the new algorithms are considerably faster than competing methods and can handle large problems and can also deal efficiently with sparse features. Expand
A working guide to boosted regression trees.
This study provides a working guide to boosted regression trees (BRT), an ensemble method for fitting statistical models that differs fundamentally from conventional techniques that aim to fit a single parsimonious model. Expand
Sparse inverse covariance estimation with the graphical lasso.
Using a coordinate descent procedure for the lasso, a simple algorithm is developed that solves a 1000-node problem in at most a minute and is 30-4000 times faster than competing methods. Expand
Generalized Additive Models
The class of generalized additive models is introduced, which replaces the linear form E fjXj by a sum of smooth functions E sj(Xj), and has the advantage of being completely auto- matic, i.e., no "detective work" is needed on the part of the statistician. Expand
Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications
Survival analyses on a subcohort of patients with locally advanced breast cancer uniformly treated in a prospective study showed significantly different outcomes for the patients belonging to the various groups, including a poor prognosis for the basal-like subtype and a significant difference in outcome for the two estrogen receptor-positive groups. Expand
An introduction to statistical learning
An introduction to statistical learning provides an accessible overview of the essential toolset for making sense of the vast and complex data sets that have emerged in science, industry, and other sectors in the past twenty years. Expand