  Pattern-mixture models provide a transparent approach for handling missing
data, where the full-data distribution is factorized in a way that explicitly
shows the parts that can be estimated from observed data alone, and the parts
that require identifying restrictions. We introduce a nonparametric estimator
of the full-data distribution based on the pattern-mixture model factorization.
Our approach uses the empirical observed-data distribution and augments it with
a nonparametric estimator of the missing-data distributions under a given
identifying restriction. Our results apply to a large class of donor-based
identifying restrictions that encompasses commonly used ones and can handle
both monotone and nonmonotone missingness. We propose a Monte Carlo procedure
to derive point estimates of functionals of interest, and the bootstrap to
construct confidence intervals.
