On the robustness of the adaptive lasso to model misspecification.

ABSTRACT: Penalization methods have been shown to yield both consistent variable selection and oracle parameter estimation under correct model specification. In this article, we study such methods under model misspecification, where the assumed form of the regression function is incorrect, including generalized linear models for uncensored outcomes and the proportional hazards model for censored responses. Estimation with the adaptive least absolute shrinkage and selection operator, lasso, penalty is proven to achieve sparse estimation of regression coefficients under misspecification. The resulting estimators are selection consistent, asymptotically normal and oracle, where the selection is based on the limiting values of the parameter estimators obtained using the misspecified model without penali

SUBMITTER: Lu W 

PROVIDER: S-EPMC4188068 | biostudies-literature | 2012 Sep

REPOSITORIES: biostudies-literature