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

… downsampled state (E1 cells n = 2394, E2 cells n = 3284, E3 cells n = 1316, pEMT1 cells …
transition distributions (bootstrap analysis based on n = 10,000 cells for each of seven …

… Hence we build upon the hypothesis-splitting idea of Ignatiadis et al. (2016) and demonstrate
that it can be used not merely as a heuristic, but instead as a theoretically grounded and …

A growing body of work uses the paradigm of algorithmic fairness to frame the development
of techniques to anticipate and proactively mitigate the introduction or exacerbation of health …

We study how to combine p-values and e-values, and design multiple testing procedures
where both p-values and e-values are available for every hypothesis. Our results provide a …

… The number of data points for calibration was N = 714. (c) A boxplot of the distribution of
best estimates for parameter values obtained for the four IndC variants. (d) A surface plot color-…

… Further suppose that the sequence of weighting kernels γ(n) … This dataset has scaled
mathematics test scores for n = 18, 174 children from kindergarten to fifth grade. Furthermore, each …

This chapter is accompanied by survlearners, a package that provides well-documented
implementations of the conditional average treatment effects (CATE) estimation strategies …

We study methods for simultaneous analysis of many noisy experiments in the presence of
rich covariate information. The goal of the analyst is to optimally estimate the true effect …

… In Section 2 we explain how for common choices of G and F n ( α ) , (7) can be computed by
… n = F n ( α n ) with α n → 0 and a pilot estimate f n ( · ) of the marginal density f G ( · ) (5). F n …