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Multiple permutation test for high-dimensional data: a components-combined algorithm.
- Source :
-
Journal of Statistical Computation & Simulation . Mar2019, Vol. 89 Issue 4, p686-707. 22p. - Publication Year :
- 2019
-
Abstract
- Multiple permutation testing is a test method combining the idea of permutation and multiple testing. It first employs the permutation testing to calculate p-values for single tests, and then determines the result based on criteria of multiple testing. To well control type I error rate, the classical method needs a large number of permutation samples for calculating p-values. When the dimension of data, m, is high, the permutation procedure is very time consuming. This paper proposes a components-combined algorithm for the type I error rate control. The new algorithm only requires a small and fixed number of permutation samples for any dimension of data and can achieve the same approximation accuracy of p-values as the classical method. Therefore, it reduces the computational amount of multiple permutation testing procedures from to . The algorithm is then applied to several testing problems and the power performance is examined by simulations and comparisons with existing methods. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00949655
- Volume :
- 89
- Issue :
- 4
- Database :
- Academic Search Index
- Journal :
- Journal of Statistical Computation & Simulation
- Publication Type :
- Academic Journal
- Accession number :
- 134434020
- Full Text :
- https://doi.org/10.1080/00949655.2019.1571058