In sparse large-scale testing problems where the false discovery proportion (FDP) is highly variable, the false discovery exceedance (FDX) provides a valuable alternative to the widely used false discovery rate (FDR). We develop an empirical Bayes approach to controlling the FDX. We show that...

Due to rapid technological advances, researchers are now able to collect and analyze ever larger data sets. Statistical inference for big data often requires solving thousands or even millions of parallel inference problems simultaneously. This poses significant challenges and calls for new...

This dissertation studies the large-scale multiple testing problem from a compound decision theoretical view, and proposes a new class of powerful data-driven procedures that substantially outperform the traditional p -value based approaches. There are several important implications from my...

type="main" xml:id="rssb12064-abs-0001" <title type="main">Summary</title> <p>The paper develops a unified theoretical and computational framework for false discovery control in multiple testing of spatial signals. We consider both pointwise and clusterwise spatial analyses, and derive oracle procedures which optimally...</p>