There is an innate human tendency, one might call it the “league table mentality,” to
construct rankings. Schools, hospitals, sports teams, movies, and myriad other objects are …

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 …

Two-sample multiple testing has a wide range of applications. The conventional practice first
reduces the original observations to a vector of p-values and then chooses a cut-off to adjust …

We investigate fairness in classification, where automated decisions are made for
individuals from different protected groups. In high-consequence scenarios, decision errors …

Replicability is the cornerstone of modern scientific research. Reliable identifications of
genotype-phenotype associations that are significant in multiple genome-wide association …

Large-scale multiple testing is a fundamental problem in high dimensional statistical
inference. It is increasingly common that various types of auxiliary information, reflecting the …

Effective control of false discovery rate is key for multiplicity problems. Here, we consider
incorporating informative covariates from external datasets in the multiple testing procedure …

An early example of a compound decision problem of Robbins (1951) is employed to
illustrate some features of the development of empirical Bayes methods. Our primary …

Economists are often interested in identifying effective policies or treatments together with
subpopulations of individuals who respond positively (or with a sign that is expected) to …

Standardization has been a widely adopted practice in multiple testing, for it takes into
account the variability in sampling and makes the test statistics comparable across different …