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of 665
pro vyhledávání: '"Goeman, Jelle"'
Integrated analysis of multi-omics datasets holds great promise for uncovering complex biological processes. However, the large dimension of omics data poses significant interpretability and multiple testing challenges. Simultaneous Enrichment Analys
Externí odkaz:
http://arxiv.org/abs/2410.19523
In many applied sciences a popular analysis strategy for high-dimensional data is to fit many multivariate generalized linear models in parallel. This paper presents a novel approach to address the resulting multiple testing problem by combining a re
Externí odkaz:
http://arxiv.org/abs/2403.02065
Despite the versatility of generalized linear mixed models in handling complex experimental designs, they often suffer from misspecification and convergence problems. This makes inference on the values of coefficients problematic. To address these ch
Externí odkaz:
http://arxiv.org/abs/2401.17993
Autor:
Li, Jinzhou, Goeman, Jelle J
Invariant causal prediction (ICP, Peters et al. (2016)) provides a novel way for identifying causal predictors of a response by utilizing heterogeneous data from different environments. One notable advantage of ICP is that it guarantees to make no fa
Externí odkaz:
http://arxiv.org/abs/2401.03834
Two permutation-based methods for simultaneous inference on the proportion of active voxels in cluster-wise brain imaging analysis have recently been published: Notip (Blain et al. 2022) and pARI (Andreella et al. 2023). Both rely on the definition o
Externí odkaz:
http://arxiv.org/abs/2307.02115
We propose new methods to obtain simultaneous false discovery proportion bounds for knockoff-based approaches. We first investigate an approach based on Janson and Su's $k$-familywise error rate control method and interpolation. We then generalize it
Externí odkaz:
http://arxiv.org/abs/2212.12822
Generalized linear models usually assume a common dispersion parameter, an assumption that is seldom true in practice. Consequently, standard parametric methods may suffer appreciable loss of type I error control. As an alternative, we present a semi
Externí odkaz:
http://arxiv.org/abs/2209.13918
We introduce a multiple testing procedure that controls the median of the proportion of false discoveries (FDP) in a flexible way. The procedure only requires a vector of p-values as input and is comparable to the Benjamini-Hochberg method, which con
Externí odkaz:
http://arxiv.org/abs/2208.11570
Autor:
Goeman, Jelle J., Górecki, Paweł\, Monajemi, Ramin, Chen, Xu, Nichols, Thomas E., Weeda, Wouter
Cluster inference based on spatial extent thresholding is the most popular analysis method for finding activated brain areas in neuroimaging. However, the method has several well-known issues. While powerful for finding brain regions with some activa
Externí odkaz:
http://arxiv.org/abs/2208.04780
Autor:
Goeman, Jelle, Solari, Aldo
We investigate a class of methods for selective inference that condition on a selection event. Such methods follow a two-stage process. First, a data-driven (sub)collection of hypotheses is chosen from some large universe of hypotheses. Subsequently,
Externí odkaz:
http://arxiv.org/abs/2207.13480