Monday, April 16, 2018 at 11:00am to 12:00pm
Weill Hall, 226
Bundling data from related testing units, and other tools for high-dimensional inference
Contemporary applications of statistics continue to fuel research in methodologies for high-dimensional hypothesis testing. One approach to increase the amount of data (and thus power) for a unit on test is to merge data from other units having similar data characteristics. I will present one version of this approach in the context where units are associated with nodes of an undirected graph; I will present findings on the sampling properties of the test statistics, and preliminary numerical results. Time permitting I will also discuss empirical Bayes approaches to testing and ranking effects.