Large-Scale Statistical Inference: Multiple Testing (2015-2017)

Multiple testing procedures are among the most important statistical tools for the analysis of modern data. We will develop new methods for providing more powerful simultaneous tests while controlling the proportion of false positive conclusions. They are to be derived by the novel pooling of information in individual attribute based contrasts to produce a Weighted Individual attribute-Specific Contrast (WISC) based statistic. They will also exploit contextual information. They will be of direct application to the problem of testing for no differences between two or more classes, as in the detection of differential expression in bioinformatics. Other key applications include biomedicine, economics, finance, genetics, and neuroscience.