Hongyuan Cao
Assistant Professor
Department of Statistics
University of Missouri
Columbia, 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 utilized. 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.