Collaborative Research: Constructing New Multiple Testing Methods

New Jersey Institute Of Technology, Newark NJ

Investigators

Abstract

Multiple hypothesis testing has become an increasingly active area of research because of its usefulness as a statistical tool to analyze data from modern scientific investigations, such as DNA microarray and functional magnetic resonance imaging (fMRI) studies. While several statistical methods have been put forward to address different multiple testing problems arising in these studies, often they were developed without fully utilizing the fact that the underlying test statistics are dependent, although such dependence is a natural phenomenon for data from these studies and might lead to misleading conclusions if not properly taken into consideration. The proposed research project seeks to develop new and innovative multiple testing methods by properly addressing the so called `dependence issues?. It focuses on three broad areas of research: (i) developing new multiple testing methods controlling generalized versions of some standard error rates that allow a few false rejections (ii) developing new multiple testing methods controlling multiple false directional errors, and (iii) developing new data-adaptive methods controlling the familywise error and false discovery rates. This project will be expected to have a broad impact on the theory and practice of statistics. The results from this project will be of importance to virtually any statistical investigation where questions are posed in terms of testing several hypotheses. For instance, in microarray or fMRI studies where detection of differentially expressed genes or active voxels is often framed as a multiple testing problem, in pharmaceutical investigations where multiple testing techniques are routinely used in dose-response study or in evaluating a drug's efficacy over standard drug or placebo, our project can potentially offer new and improved methodologies. The project would also benefit education through training of graduate students, incorporation of the developed methodologies in statistics courses. The results will be disseminated through presentations and discussions at national and international conferences, and visits to other institutions. The software to be developed under this project will be made available, free of charge, to the scientific community.