The lasso and its variants have attracted much attention recently because of its ability of simultaneous estimation and … linearly constrained generalized lasso, where the constraints are either linear inequalities or equalities or both. The dual of …

approximate sparsity of the spatial weights matrix. The proposed estimation methodology exploits the Lasso estimator and mimics … variables is larger than the number of observations. We derive convergence rates for the two-step Lasso estimator. Our Monte …

-Sampling approach (U-MIDAS) (see Foroni et al., 2015; Castle et al., 2009; Bec and Mogliani, 2013), and the LASSO-type penalised …

for the midpoints and the other one for the radii. The estimation problem is approached by introducing Lasso … midpoint and the radius models. The effectiveness of our method, called Lasso-IR (Lasso-based Interval-valued Regression), is …

This study presents a first comparative analysis of Lasso-type (Lasso, adaptive Lasso, elastic net) and heuristic … subset selection methods. Although the Lasso has shown success in many situations, it has some limitations. In particular …, inconsistent results are obtained for pairwise strongly correlated predictors. An alternative to the Lasso is constituted by model …

least absolute shrinkage and selection operator (LASSO) selection technique for choosing an appropriate subset of financial …. Finally, the authors demonstrate the effectiveness of the LASSO technique for identifying the most important financial ratios …