STATISTICS COLLOQUIUM
Hongyuan Cao, Assistant Professor
Department of Statistics
University of Missouri
Columbia
Change point estimation: another look at multiple testing problems
ABSTRACT
We consider the problem of 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 local clustering information can be utilized. We show that by exploiting the local structure, the precision of a multiple testing procedure can be improved substantially. 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 demonstrate the improved detection ability compared with competing methods. The practical utility of our methods is illustrated from a genome-wide association study of blood lipids.
DATE: Wednesday, February 17, 2016
TIME: 4:00 p.m. -5:00 p.m.
PLACE: Philip E. Austin Bldg., Rm. 105
Coffee will be served at 3:30 p.m. in the Noether Lounge (AUST 326)
For more information, contact: Tracy Burke at tracy.burke@uconn.edu