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• W212980374 abstract "To estimate a sparse linear model from data with Gaussian noise, consilience from lasso and compressed sensing literatures is that thresholding estimators like lasso and the Dantzig selector have the ability in some situations to identify with high probability part of the significant covariates asymptotically, and are numerically tractable thanks to convexity. Yet, the selection of a threshold parameter $lambda$ remains crucial in practice. To that aim we propose Quantile Universal Thresholding, a selection of $lambda$ at the detection edge. We show with extensive simulations and real data that an excellent compromise between high true positive rate and low false discovery rate is achieved, leading also to good predictive risk." @default.
• W212980374 created "2016-06-24" @default.
• W212980374 creator A5089372679 @default.
• W212980374 creator A5090065325 @default.
• W212980374 date "2014-12-05" @default.
• W212980374 modified "2023-09-27" @default.
• W212980374 title "Quantile universal threshold: model selection at the detection edge for high-dimensional linear regression" @default.
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• W212980374 hasPublicationYear "2014" @default.
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