Title（标题）：On the robustness of penalized variable selection to model misspecification

Speaker（汇报人）：Prof.Jason Fine, The University of North Carolina at Chapel Hill

Time（功夫）：2012年7月6日（周五）下午02:00-3:00

Place（地址）：拉斯维加斯9888新楼216教室

Abstract（提要）：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 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 penalization. We further derive conditions under which the penalized estimators from the misspecified model may yield selection consistency under the true model. The robustness issue is explored numerically via extensive simulations and an application to the Wisconsin Epidemiological Study of Diabetic Retinopathy.

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