Seminar by Prof. Yanyuan MA from Department of Statistics, The Pennsylvania State University

We consider the estimation problem in a regression setting where the outcome variable is subject to nonignorable missingness and identifiability is ensured by the shadow variable approach.

We propose a versatile estimation procedure where modeling of missingness mechanism is completely bypassed.

We show that our estimator is easy to implement and we derive the asymptotic theory of the proposed estimator.

We also investigate some alternative estimators under different scenarios.

Comprehensive simulation studies are conducted to demonstrate the finite sample performance of the method.

We apply the estimator to a children's mental health study to illustrate its usefulness.

Prof. Yanyuan Ma is a Professor of Statistics at Penn State. Ma received her Ph.D. in Applied Mathematics from MIT in 1999. She received her B.S. in Mathematics from Beijing University in 1994. Her research interest is in measurement error models, dimension reduction, mixed sample problems, latent variable models, selection bias and skew-elliptical distributions, missing not at random problems and more generally semiparametrics. She is currently a fellow of the Institute of Mathematical Statistics and the American Statistical Association.