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A Uniform Improvement of the Benjamini-Hochberg Procedure via e-Closure
Jelle Goeman
This paper presents closed BH, a uniform improvement of the False Discovery Rate controlling method of Benjamini and Hochberg (BH). Closed BH is valid under the same assumption of…
On the error control of invariant causal prediction
Jinzhou Li, Jelle J Goeman
Invariant causal prediction provides a useful framework for identifying causal predictors of a response using heterogeneous data from multiple environments. One valuable property o…
Bringing Closure to False Discovery Rate Control: A General Principle for Multiple Testing
Ziyu Xu, Aldo Solari, Lasse Fischer +3
We present a novel necessary and sufficient principle for multiple testing methods controlling an expected loss. This principle asserts that every such multiple testing method is a…
Multivariate longitudinal modeling of cross-sectional and lagged associations between a continuous time-varying endogenous covariate and a non-Gaussian outcome
Chiara Degan, Bart J. A. Mertens, Jelle Goeman +4
In longitudinal studies, time-varying covariates are often endogenous, meaning their values depend on both their own history and that of the outcome variable. This violates key ass…
Robust Inference for Generalized Linear Mixed Models: An Approach Based on Score Sign Flipping
Angela Andreella, Jelle Goeman, Jesse Hemerik +1
Despite the versatility of generalized linear mixed models in handling complex experimental designs, they often suffer from misspecification and convergence problems. This makes in…
Bad estimation, good prediction: the Lasso in dense regimes
Andrea Bratsberg, Magne Thoresen, Jelle J. Goeman
For high-dimensional omics data, sparsity-inducing regularization methods such as the Lasso are widely used and often yield strong predictive performance, even in settings when the…