Rights statement: © Springer Science+Business Media New York 2016. The final publication is available at Springer via http://dx.doi.org/10.1007/s11222-016-9656-z
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Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
<mark>Journal publication date</mark> | 4/05/2017 |
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<mark>Journal</mark> | Statistics and Computing |
Issue number | 3 |
Volume | 27 |
Number of pages | 10 |
Pages (from-to) | 823-832 |
Publication Status | Published |
Early online date | 1/05/16 |
<mark>Original language</mark> | English |
Multiple hypothesis testing is widely used to evaluate scientific studies involving statistical tests. However, for many of these tests, p values are not available and are thus often approximated using Monte Carlo tests such as permutation tests or bootstrap tests. This article presents a simple algorithm based on Thompson Sampling to test multiple hypotheses. It works with arbitrary multiple testing procedures, in particular with step-up and step-down procedures. Its main feature is to sequentially allocate Monte Carlo effort, generating more Monte Carlo samples for tests whose decisions are so far less certain. A simulation study demonstrates that for a low computational effort, the new approach yields a higher power and a higher degree of reproducibility of its results than previously suggested methods.