Co-sufficient weighting: leveraging sparsity for more powerful variable selection in the linear model

We show that for the problem of controlled variable selection in the Gaussian linear model, informative and p-value-independent weights (for weighted multiple testing) can be derived entirely from sufficient statistics and a belief in sparsity using only the data itself and no external quantitative