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View Submission - CMStatistics
B0776
Title: How not to estimate the nonignorable missingness mechanism Authors:  Jiwei Zhao - State University of New York at Buffalo (United States) [presenting]
Yanyuan Ma - Pennsylvania State University (United States)
Abstract: The estimation problem is considered 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 the modeling of the missingness mechanism is completely bypassed. We show that the proposed estimator is easy to implement, and we derive its asymptotic theory. 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.