  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.
