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Thresholding based tests for null hypothesis of the form A beta =c, and the Quantile Universal Threshold (QUT) for lasso regularization of Generalized Linear Models (GLM) and square-root lasso to obtain a sparse model with a good compromise between high true positive rate and low false discovery rate. Giacobino et al. (2017) <doi:10.1214/17-EJS1366>. Sardy et al. (2017) <arXiv:1708.02908>.
Package details |
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Author | Jairo Diaz-Rodriguez [aut, cre, cph], Sylvain Sardy [aut, ths], Caroline Giacobino [aut], Nick Hengartner [aut] |
Maintainer | Jairo Diaz-Rodriguez <adjairo@uninorte.edu.co> |
License | GPL-2 |
Version | 2.2 |
Package repository | View on CRAN |
Installation |
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