58 citations · 122 across the 11 of their papers we have counts for
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stat.ML2014★ 12 cited
On the Sensitivity of the Lasso to the Number of Predictor Variables
Cheryl J. Flynn, Clifford M. Hurvich, Jeffrey S. Simonoff
The Lasso is a computationally efficient regression regularization procedure that can produce sparse estimators when the number of predictors (p) is large. Oracle inequalities prov…
stat.ML2013★ 58 cited
Efficiency for Regularization Parameter Selection in Penalized Likelihood Estimation of Misspecified Models
Cheryl J. Flynn, Clifford M. Hurvich, Jeffrey S. Simonoff
It has been shown that AIC-type criteria are asymptotically efficient selectors of the tuning parameter in non-concave penalized regression methods under the assumption that the po…