Daniel F. Heitjan, Columbia University
Ignorability and the Coarse-Data ModelWhen a fraction of the
data are missing, it is common practice to act as though there had
never been any intention of observing the units that were missed.
This does not spoil inferences, provided the missing data are
"ignorable". In many situations--for example, censored survival
data--we do not know the precise values of the observations, but they
are not exactly missing either, so it is not clear precisely how to
express the idea of ignorability. I will describe the coarse-data
model, a general model for parametric inference that permits us to
extend ignorability to a broader range of incomplete-data problems.
If time permits, I will describe the relationship of ignorability to
the concepts of sufficient and ancillary statistics.