PhD Seminar: The performance of procedures in high-dimensional multiple hypothesis testing in the presence of dependence
by Sandy Clarke
Abstract: Multiple hypothesis testing is a research area that has grown considerably in recent years as the amount of data available to statisticians grows, from a variety of applications. High-dimensional contexts have their own challenges, in particular, developing testing procedures which detect true effects powerfully whilst keeping the rate of false positives (such as the false discovery rate or FDR) low. In these contexts, the assumption of independence between test statistics is commonly made, although this is rarely true. This talk will discuss the ramifications of this assumption, in the context of a linear process. I will summarise some of the existing results in this area and go on to describe those arising from my PhD project.
For More Information: Contact Paul Pearce (P.Pearce@ms.unimelb.edu.au) or Paul Norbury (P.Norbury@ms.unimelb.edu.au)