Authors: Dette, Holger
Wagener, Jens
Title: Bridge estimators and the adaptive Lasso under heteroscedasticity
Language (ISO): en
Abstract: In this paper we investigate penalized least squares methods in linear regression models with heteroscedastic error structure. It is demonstrated that the basic properties with respect to model selection and parameter estimation of bridge estimators, Lasso and adaptive Lasso do not change if the assumption of homoscedasticity is violated. However, these estimators do not have oracle properties in the sense of Fan and Li (2001). In order to address this problem we introduce weighted penalized least squares methods and demonstrate their advantages by asymptotic theory and by means of a simulation study.
Subject Headings: adaptive Lasso
asymptotic normality
bridge estimators
conservative model selection
oracle property
Issue Date: 2011-07-04
Appears in Collections:Sonderforschungsbereich (SFB) 823

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