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Estimate the mean of a Gaussian vector, by choosing among a large collection of estimators, following the method developed by Y. Baraud, C. Giraud and S. Huet (2014) <doi:10.1214/13-AIHP539>. In particular it solves the problem of variable selection by choosing the best predictor among predictors emanating from different methods as lasso, elastic-net, adaptive lasso, pls, randomForest. Moreover, it can be applied for choosing the tuning parameter in a Gauss-lasso procedure.
Package details |
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Author | Yannick Baraud, Christophe Giraud, Sylvie Huet |
Maintainer | Benjamin Auder <benjamin.auder@universite-paris-saclay.fr> |
License | GPL (>= 3) |
Version | 1.1.5 |
Package repository | View on CRAN |
Installation |
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