Abstract
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.
Supplementary Materials
The supplementary materials contain all the detailed technical derivations and proofs.
Acknowledgments
The authors would like to thank the editor, an associate editor, and three reviewers for their insightful comments which have helped improve the manuscript substantially.