  It is argued that the Calibrated Bayesian (CB) approach to statistical
inference capitalizes on the strength of Bayesian and frequentist approaches to
statistical inference. In the CB approach, inferences under a particular model
are Bayesian, but frequentist methods are useful for model development and
model checking. In this article the CB approach is outlined. Bayesian methods
for missing data are then reviewed from a CB perspective. The basic theory of
the Bayesian approach, and the closely related technique of multiple
imputation, is described. Then applications of the Bayesian approach to normal
models are described, both for monotone and nonmonotone missing data patterns.
Sequential Regression Multivariate Imputation and Penalized Spline of
Propensity Models are presented as two useful approaches for relaxing
distributional assumptions.
