  A popular framework for false discovery control is the random effects model
in which the null hypotheses are assumed to be independent. This paper
generalizes the random effects model to a conditional dependence model which
allows dependence between null hypotheses. The dependence can be useful to
characterize the spatial structure of the null hypotheses. Asymptotic
properties of false discovery proportions and numbers of rejected hypotheses
are explored and a large-sample distributional theory is obtained.
