Journal Article

The Adaptive Lasso and Its Oracle Properties

Zou, Hui

2006

Overview

The lasso is a popular technique for simultaneous estimation and variable selection. Lasso variable selection has been shown to be consistent under certain conditions. In this work we derive a necessary condition for the lasso variable selection to be consistent. Consequently, there exist certain scenarios where the lasso is inconsistent for variable selection. We then propose a new version of the lasso, called the adaptive lasso, where adaptive weights are used for penalizing different coefficients in the ℓ 1 penalty. We show that the adaptive lasso enjoys the oracle properties; namely, it performs as well as if the true underlying model were given in advance. Similar to the lasso, the adaptive lasso is shown to be near-minimax optimal. Furthermore, the adaptive lasso can be solved by the same efficient algorithm for solving the lasso. We also discuss the extension of the adaptive lasso in generalized linear models and show that the oracle properties still hold under mild regularity conditions. As a byproduct of our theory, the nonnegative garotte is shown to be consistent for variable selection.

• Publisher: Taylor & Francis,American Statistical Association,Assoc,Taylor & Francis Ltd
• Subject: Algorithms / Applications / Asymptotic normality / Decision theory / Estimation / Estimators / Exact sciences and technology / Feature selection / Forecasts / General topics / Generalized linear model / Generalized linear models / Lasso / Linear analysis / Linear models / Lot quality assurance sampling / Mathematics / Minimax / Modeling / Necessary conditions / Oracle inequality / Oracle procedure / Oracles / Parameter estimation / Parametric inference / Probability and statistics / Property / Regression analysis / Reptiles & amphibians / Sciences and techniques of general use / Simulations / Standard error / Statistical methods / Statistics / Supernova remnants / Theory and Methods / Threshing / Variable selection / Variables