Adaptive Lasso
Found 3 free book(s)The Adaptive Lasso and Its Oracle Properties
pages.cs.wisc.eduadaptive lasso in Section 3, and then prove its statistical prop-erties. We also show that the nonnegative garotte is consistent for variable selection. We apply the LARS algorithm (Efron, Hastie, Johnstone, and Tibshirani 2004) to solve the entire so-lution path of the adaptive lasso. We use a simulation study to
Modern regression 2: The lasso - Carnegie Mellon University
www.stat.cmu.edu^lasso = argmin 2Rp ky X k2 2 + k k 1 Thetuning parameter controls the strength of the penalty, and (like ridge regression) we get ^lasso = the linear regression estimate when = 0, and ^lasso = 0 when = 1 For in between these two extremes, we are balancing two ideas: tting a linear model of yon X, and shrinking the coe cients. But the nature of ...
Variable Selection Using Random Forests in SAS®
www.sas.comrates that compare favorably to Adaboost (short for “Adaptive Boosting”). Proposed by Freund and Schapire in 1996, Adaboost is a practical boosting algorithm focusing on classification problems and aims to create a strong classifier by converting a set of weak classifiers.