15,898 research outputs found
A Permutation Approach for Selecting the Penalty Parameter in Penalized Model Selection
We describe a simple, efficient, permutation based procedure for selecting
the penalty parameter in the LASSO. The procedure, which is intended for
applications where variable selection is the primary focus, can be applied in a
variety of structural settings, including generalized linear models. We briefly
discuss connections between permutation selection and existing theory for the
LASSO. In addition, we present a simulation study and an analysis of three real
data sets in which permutation selection is compared with cross-validation
(CV), the Bayesian information criterion (BIC), and a selection method based on
recently developed testing procedures for the LASSO
A comparative study of the Lasso-type and heuristic model selection methods
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 selection based on information criteria (IC), which remains consistent in the situation mentioned. However, these criteria are hard to optimize due to a discrete search space. To overcome this problem, an optimization heuristic (Genetic Algorithm) is applied. Monte-Carlo simulation results are reported to illustrate the performance of the methods.Model selection, Lasso, adaptive Lasso, elastic net, heuristic methods, genetic algorithms
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