Department of Statistics
University of Missouri
Change-point estimation: another look at multiple testing problems
We consider large scale multiple testing for data that have locally
clustered signals. With this structure, we apply techniques from
change-point analysis and propose a boundary detection algorithm so that
the clustering information can be utilised. Consequently the precision of
the multiple testing procedure is substantially improved. We study tests
with independent as well as dependent p-values. Monte Carlo simulations
suggest that the methods perform well with realistic sample sizes and show
improved detection ability compared with competing methods. Our procedure
is applied to a genome-wide association dataset of blood lipids.
Refreshments at 3:45pm in Snedecor 2101.