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The LASSO is a variable subset selection procedure in statistical linear regression based on sparsity promoting `1 penalization of the least-squares operator. In many applications, the design matrix has strongly correlated columns which are smoothly evolving with the column index. For such applications, the standard LASSO does not provide satisfactory solutions in practice because some incoherence...
We address the issue of estimating the regression vector \(\beta \) in the generic \(s\) -sparse linear model \(y = X\beta +z\) , with \(\beta \in \mathbb {R}^{p}\) , \(y\in \mathbb {R}^{n}\) , \(z\sim \mathcal N(0,\sigma ^2 I)\) , and \(p> n\) when the variance \(\sigma ^{2}\) is unknown. We study two least absolute shrinkage and selection operator (LASSO)-type methods that jointly...
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