Quantile universal threshold: model selection at the detection edge for high-dimensional linear regression

5 Dec 2014 Jairo Diaz-Rodriguez Sylvain Sardy

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... (read more)

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