Leiden University Student Repository

Kisoentewari, A.M.K. (2022)

Evaluating the robustness of permutation-based multiple testing methods

Master thesis | Statistics and Data Science

Statistical hypothesis testing is central to many scientific fields. Testing many hypothe-ses simultaneously is called multiple testing. The main concern in multiple testing, is toensure that most of the rejected null hypotheses are indeed false, i.e., that the numberof incorrect rejections remains low. A major challenge in multiple testing is to accountfor the complex dependencies in the data. A powerful approach in this regard, arepermutation-based multiple testing methods. These methods make few distributionalassumptions. In fact, they often make only one assumption, called joint exchangeabil-ity. In this thesis we investigate the robustness of the methods to violations of thisassumption. We do this by means of simulations, where we focus on case-control data.We find that, while the theoretical literature always makes the mentioned assumption,it is often not necessary in practice. Thus, this thesis provides further evidence for thevalidity of these powerful methods in practice.

Multiple testing

Permutation

High-dimensional

maxT

SAM

Robustness

Heteroscedasticity

Author

Kisoentewari, A.M.K.

Faculty

Faculty of Science

Supervisors

Hemerik, J.B.A.

ECTS Credits

24

Language

en