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Activity Number: 598 - Statistical Learning with Unconventional Missing Data
Type: Topic Contributed
Date/Time: Thursday, August 1, 2019 : 8:30 AM to 10:20 AM
Sponsor: International Chinese Statistical Association
Abstract #301688
Title: How Not to Estimate the Nonignorable Missingness Mechanism
Author(s): Jiwei Zhao*
Companies: State University of New York At Buffalo
Keywords: nonignorable missing data; missingness mechanism; unconventional likelihood; conditional likelihood; pseudo likelihood; asymptotic normality

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.

Authors who are presenting talks have a * after their name.

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