Comparisons of penalized least squares methods by simulations
arXiv:1405.1796
Abstract
Penalized least squares methods are commonly used for simultaneous estimation and variable selection in high-dimensional linear models. In this paper we compare several prevailing methods including the lasso, nonnegative garrote, and SCAD in this area through Monte Carlo simulations. Criterion for evaluating these methods in terms of variable selection and estimation are presented. This paper focuses on the traditional n > p cases. For larger p, our results are still helpful to practitioners after the dimensionality is reduced by a screening method. K
References in corpus (15)
- Discussion of "Least angle regression" by Efron et al
- Rejoinder to "Least angle regression" by Efron et al
- Discussion of "Least angle regression" by Efron et al
- Discussion of "Least angle regression" by Efron et al
- Least Angle Regression
- Discussion of "Least angle regression" by Efron et al
- Discussion of "Least angle regression" by Efron et al
- Discussion of "Least angle regression" by Efron et al
- Discussion of "Least angle regression" by Efron et al
- Discussion of "Least angle regression" by Efron et al
- Nearly unbiased variable selection under minimax concave penalty
- Pathwise coordinate optimization
- Sure independence screening in generalized linear models with NP-dimensionality
- Robust rank correlation based screening
- Regularized estimation of large-scale gene association networks using graphical Gaussian models