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Found 7 papers, 6 papers with code
Large-scale simultaneous inference under dependence
Simultaneous inference allows for the exploration of data while deciding on criteria for proclaiming discoveries.
Statistics Theory Methodology Statistics Theory
The leave-one-covariate-out conditional randomization test
Conditional independence testing is an important problem, yet provably hard without assumptions.
Fast and Powerful Conditional Randomization Testing via Distillation
We propose the distilled CRT, a novel approach to using state-of-the-art machine learning algorithms in the CRT while drastically reducing the number of times those algorithms need to be run, thereby taking advantage of their power and the CRT's statistical guarantees without suffering the usual computational expense.
Methodology
On the power of conditional independence testing under model-X
For testing conditional independence (CI) of a response Y and a predictor X given covariates Z, the recently introduced model-X (MX) framework has been the subject of active methodological research, especially in the context of MX knockoffs and their successful application to genome-wide association studies.
Controlling FDR while highlighting selected discoveries
Often modern scientific investigations start by testing a very large number of hypotheses in an effort to comprehensively mine the data for possible discoveries.
Methodology
Towards "simultaneous selective inference": post-hoc bounds on the false discovery proportion
In this paper, we show that the entire path of rejection sets considered by a variety of existing FDR procedures (like BH, knockoffs, and many others) can be endowed with simultaneous high-probability bounds on FDP.
Statistics Theory Statistics Theory
Covariance estimation using conjugate gradient for 3D classification in Cryo-EM
Classifying structural variability in noisy projections of biological macromolecules is a central problem in Cryo-EM.
