ABSTRACT
This chapter provides a Bayesian perspective on how inference might proceed in settings where data that are intended to be collected are missing. Assessment of model sensitivity is a broad topic, encompassing many aspects of inference that might include distributional assumptions, parametric structure, sensitivity to outliers, and assessment of influence of individual data points. When the intended sample is completely observed, many of these
can be checked empirically; our ability to refute the assumptions with any degree of confidence is limited only by sample size, so in some sense these assumptions can be subjected to empirical critique. Assumptions required for fitting models to incomplete data are different because they apply to data that cannot be observed and are therefore inherently untestable. Put simply, they are subjective.
