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of 183
pro vyhledávání: '"Neuvial Pierre"'
Knockoffs are a popular statistical framework that addresses the challenging problem of conditional variable selection in high-dimensional settings with statistical control. Such statistical control is essential for the reliability of inference. Howe
Externí odkaz:
http://arxiv.org/abs/2407.06892
Recent work by Gao et al. has laid the foundations for post-clustering inference. For the first time, the authors established a theoretical framework allowing to test for differences between means of estimated clusters. Additionally, they studied the
Externí odkaz:
http://arxiv.org/abs/2310.11822
Controlled variable selection is an important analytical step in various scientific fields, such as brain imaging or genomics. In these high-dimensional data settings, considering too many variables leads to poor models and high costs, hence the need
Externí odkaz:
http://arxiv.org/abs/2310.10373
Classical inference methods notoriously fail when applied to data-driven test hypotheses or inference targets. Instead, dedicated methodologies are required to obtain statistical guarantees for these selective inference problems. Selective inference
Externí odkaz:
http://arxiv.org/abs/2309.01492
In this article we develop a method for performing post hoc inference of the False Discovery Proportion (FDP) over multiple contrasts of interest in the multivariate linear model. To do so we use the bootstrap to simulate from the distribution of the
Externí odkaz:
http://arxiv.org/abs/2208.13724
Publikováno v:
NeuroImage (2022), 119492
Cluster-level inference procedures are widely used for brain mapping. These methods compare the size of clusters obtained by thresholding brain maps to an upper bound under the global null hypothesis, computed using Random Field Theory or permutation
Externí odkaz:
http://arxiv.org/abs/2204.10572
Publikováno v:
J. Gonz\'alez-Delgado, A. Gonz\'alez-Sanz, J. Cort\'es, P. Neuvial. Two-sample goodness-of-fit tests on the flat torus based on Wasserstein distance and their relevance to structural biology. Electron. J. Statist., 17(1) 1547-1586, 2023
This work is motivated by the study of local protein structure, which is defined by two variable dihedral angles that take values from probability distributions on the flat torus. Our goal is to provide the space $\mathcal{P}(\mathbb{R}^2/\mathbb{Z}^
Externí odkaz:
http://arxiv.org/abs/2108.00165
We address the multiple testing problem under the assumption that the true/false hypotheses are driven by a Hidden Markov Model (HMM), which is recognized as a fundamental setting to model multiple testing under dependence since the seminal work of \
Externí odkaz:
http://arxiv.org/abs/2105.00288
Autor:
Champion, Magali, Chiquet, Julien, Neuvial, Pierre, Elati, Mohamed, Radvanyi, François, Birmelé, Etienne
Publikováno v:
Proceedings of the 12th International Conference on Bioinformatics and Computational Biology, vol 70, pages 1--10
We propose a methodology for the identification of transcription factors involved in the deregulation of genes in tumoral cells. This strategy is based on the inference of a reference gene regulatory network that connects transcription factors to the
Externí odkaz:
http://arxiv.org/abs/2004.08312
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