In the current climate of analysis of massive data sets,
there areconcerns about how well methods "scale up."
By most accounts, classical FWE-controlling methods do
not scale up well to increasing p, while FDR-controlling
methods do.  On the other hand, neither method scales up
well with increasing n for typical point null testing.
If priors and losses are correct, and if computation is
feasible, scaling is not an issue for decision-theoretic
methods. I will investigate FWE-controlling methods and
FDR-controlling methods, as well as a newer proposal of
Brad Efron, from the standpoint of minimizing loss, for
different types of realistic loss functions.

Joint work with Ananda Bandulasiri