Permutation-based inference for high-dimensional data.

Hemerik, J. (2018)

Doctoral Thesis

The thesis provides novel statistical (multiple) testing methods based on permutations or other transformations of data. Permutation-based multiple testing methods tend to have relatively high power, because they take into account the observed dependence structure of the data (without requiring assumptions on that structure). This thesis places particular emphasis on confidence upper bounds for the false discovery proportion: the proportion of false positives among all rejected hypotheses. Moreover, a novel semi-parametric method is presented for robust testing in generalized linear models. This test is related to the score test and the permutation test and is based on sign-flipping individual score contributions.

• Permutation'
• Non-parametric test
• Multiple testing
• False discovery proportion
• False discovery rate
• Confidence bound
• Robust test
• Significance analysis of microarrays
• SAM
• Semi-parametric

• All authors: Hemerik, J.
• Supervisor: Goeman, J.J.; Solari, A.
• Committee: Cessie, S. le; Vaart, A.W. van der; Wiel, M.A. van de
• Qualification: Doctor (dr.)
• Awarding Institution: Medicine, Leiden University Medical Center (LUMC), Leiden University
• Date: 2018-05-01
• ISBN (print): 9789461828859