Title: DAGGER: A sequential algorithm for FDR control on DAGs,
Abstract: We propose a linear-time, single-pass, top-down algorithm for multiple
testing on directed acyclic graphs (DAGs), where nodes represent hypotheses and
edges specify a partial ordering in which hypotheses must be tested. The
procedure is guaranteed to reject a sub-DAG with bounded false discovery rate
(FDR) while satisfying the logical constraint that a rejected node's parents
must also be rejected. It is designed for sequential testing settings, when the
DAG structure is known a priori, but the $p$-values are obtained selectively
(such as in a sequence of experiments), but the algorithm is also applicable in
non-sequential settings when all $p$-values can be calculated in advance (such
as variable/model selection). Our DAGGER algorithm, shorthand for Greedily
Evolving Rejections on DAGs, provably controls the false discovery rate under
independence, positive dependence or arbitrary dependence of the $p$-values.
The DAGGER procedure specializes to known algorithms in the special cases of
trees and line graphs, and simplifies to the classical Benjamini-Hochberg
procedure when the DAG has no edges. We explore the empirical performance of
DAGGER using simulations, as well as a real dataset corresponding to a gene
ontology, showing favorable performance in terms of time and power.