OBJECTIVE COVID-19 has become a major public health problem. There is good evidence
that ACE2 is a receptor for SARS-CoV-2, and high expression of ACE2 may increase …

Hypothesis weighting improves the power of large-scale multiple testing. We describe
independent hypothesis weighting (IHW), a method that assigns weights using covariates …

Background In high-throughput studies, hundreds to millions of hypotheses are typically
tested. Statistical methods that control the false discovery rate (FDR) have emerged as …

Scientists from nearly all disciplines face the problem of simultaneously evaluating many
hypotheses. Conducting multiple comparisons increases the likelihood that a non-negligible …

Bayesian Inference and Testing of Group Differences in Brain Networks Page 1 Bayesian Analysis
(2018) 13, Number 1, pp. 29–58 Bayesian Inference and Testing of Group Differences in Brain …

Introduction Despite an impressive amount of metabolomics studies in animal models and
humans, most findings have not yet translated into the clinical setting, and the road ahead …

While statistical analysis of a single network has received a lot of attention in recent years,
with a focus on social networks, analysis of a sample of networks presents its own …

We propose a deep learning–based knockoffs inference framework, DeepLINK, that
guarantees the false discovery rate (FDR) control in high-dimensional settings. DeepLINK is …

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 …

Modern scientific studies from many diverse areas of research abound with multiple
hypothesis testing concerns. The false discovery rate (FDR) is one of the most commonly …