Department of Statistics Seminar
North Carolina State University

presents

Dr. Hua Yun Chen

University of Illinois at Chicago

"Approximation to locally semiparametric efficient scores in missing data problems through likelihood robustification"

ABSTRACT

In semiparametric models with missing data, the semiparametric efficient estimator often cannot be obtained without additional model assumption even if the semiparametric efficient estimator has simple form when no missing data are involved. Robins et al. proposed to find the locally semiparametric efficient estimator as a compromise and showed that the locally semiparametric efficient estimator have the doubly robust property when the missing data are missing at random in Rubin's sense. In practice, the approach proposed by Robins et al. to finding an locally semiparametric efficient estimator can be very challenge to implement. We proposed an approach to approximating locally semiparametric efficient scores through likelihood robustification. The proposed approach is flexible, is relatively easy to implement, and can be applied to missing data with arbitrary missing patterns. The approximation estimator has the doubly robust property when missing data are MAR, and only requires correct specification of the missing data mechanism model for consistency when missing data are nonignorable. Estimation and inferences on the parameters are proposed. Applications of the proposed method are illustrated by examples, including doubly robust inferences in parametric regression and in Cox regression with missing data, and robust inferences against misspecification of random effects distribution in random effects models and in frailty models. The performance of the approach is examined by a simulation study.

Friday, April, 8, 2005

3:35 - 4:35 pm

206 Cox Hall

Refreshments will be served on the second floor of Dabney Hall (left of Room 222) at 3:00 pm.