Statistics and Data Science Seminar
Hongyuan Cao
University of Chicago
On Multiple Testing and the Monotone Likelihood Ratio Condition
Abstract: High-throughput screening has become an important mainstay for con-
temporary biomedical research. A standard approach is to get p-values and
adjust for multiple comparison in a manner that controls false discovery rate
(FDR). The concavity of p-value distribution under the alternative has been
a standard condition for developing many FDR procedures: Storey (2003),
Genovese and Wasserman (2004), Kosorok and Ma (2007). A more general
concept is the monotone likelihood ratio condition (MLRC) introduced in
Sun and Cai (2007). We show in this paper that the concavity assumption
can be violated for (i) a simple heteroscedastic normal mixture model and
(ii) dependent tests. Some interesting implications, including different testing procedures (step-up vs step-down), the choice of test statistic and the
power definition in multiple testing are discussed. This is joint work with
Wenguang Sun and Michael R. Kosorok.
Wednesday November 3, 2010 at 3:00 PM in SEO 636