NDLI: Bridge estimators and the adaptive lasso under heteroscedasticity

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Bridge estimators and the adaptive lasso under heteroscedasticity

Content Provider	Springer Nature Link
Author	Wagener, J. Dette, H.
Copyright Year	2012
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) if the oracle is based on weighted least squares. 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.
Starting Page	109
Ending Page	126
Page Count	18
File Format	PDF
ISSN	10665307
Journal	Mathematical Methods of Statistics
Volume Number	21
Issue Number	2
e-ISSN	19348045
Language	English
Publisher	Allerton Press, Inc.
Publisher Date	2012-07-08
Publisher Place	Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Lasso adaptive bridge estimators heteroscedasticity asymptotic normality conservative model selection oracle property Ridge regression; shrinkage estimators Asymptotic properties of estimators Statistical Theory and Methods
Content Type	Text
Resource Type	Article
Subject	Statistics and Probability Statistics, Probability and Uncertainty

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