Zobrazeno 1 - 10
of 547
pro vyhledávání: '"GOEMAN, Jelle J."'
The multiple testing problem appears when fitting multivariate generalized linear models for high dimensional data. We show that the sign-flip test can be combined with permutation-based procedures for assessing the multiple testing problem
Externí odkaz:
http://arxiv.org/abs/2403.02065
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. This assumption is seldom true in practice, and may cause appreciable loss of type I error control if standard parametric methods are used. We present an alternative semi-paramet
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
Classical cluster inference is hampered by the spatial specificity paradox. Given the null-hypothesis of no active voxels, the alternative hypothesis states that there is at least one active voxel in a cluster. Hence, the larger the cluster the less
Externí odkaz:
http://arxiv.org/abs/2206.13587
We propose a novel resampling-based method to construct an asymptotically exact test for any subset of hypotheses on coefficients in high-dimensional linear regression. It can be embedded into any multiple testing procedure to make confidence stateme
Externí odkaz:
http://arxiv.org/abs/2205.12563
Autor:
Sluiskes, Marije H.1 (AUTHOR) m.h.sluiskes@lumc.nl, Goeman, Jelle J.1 (AUTHOR), Beekman, Marian2 (AUTHOR), Slagboom, P. Eline2,3 (AUTHOR), Putter, Hein1 (AUTHOR), Rodríguez-Girondo, Mar1 (AUTHOR)
Publikováno v:
BMC Medical Research Methodology. 3/8/2024, Vol. 24 Issue 1, p1-15. 15p.
Publikováno v:
J. R. Stat. Soc. Series B Stat. Methodol. (2023)
Sum-based global tests are highly popular in multiple hypothesis testing. In this paper we propose a general closed testing procedure for sum tests, which provides lower confidence bounds for the proportion of true discoveries (TDP), simultaneously o
Externí odkaz:
http://arxiv.org/abs/2102.11759
Autor:
Kapteijn, Maaike Y., Lanting, Vincent R., Kaptein, Fleur H.J., Guman, Noori A.M., Laghmani, El Houari, Kuipers, Thomas B., Mei, Hailiang, Goeman, Jelle J., Mulder, Frits I., van Duinen, Sjoerd G., Taphoorn, Martin J.B., Dirven, Linda, Broekman, Marike L.D., van Es, Nick, Klok, Frederikus A., Koekkoek, Johan A.F., Versteeg, Henri H., Buijs, Jeroen T.
Publikováno v:
In Thrombosis Research December 2023 232:27-34