Literature DB >> 30930620

Exact testing with random permutations.

Jesse Hemerik1, Jelle Goeman1.   

Abstract

When permutation methods are used in practice, often a limited number of random permutations are used to decrease the computational burden. However, most theoretical literature assumes that the whole permutation group is used, and methods based on random permutations tend to be seen as approximate. There exists a very limited amount of literature on exact testing with random permutations, and only recently a thorough proof of exactness was given. In this paper, we provide an alternative proof, viewing the test as a "conditional Monte Carlo test" as it has been called in the literature. We also provide extensions of the result. Importantly, our results can be used to prove properties of various multiple testing procedures based on random permutations.

Entities:  

Keywords:  Nonparametric test; Permutation test; Resampling

Year:  2017        PMID: 30930620      PMCID: PMC6405018          DOI: 10.1007/s11749-017-0571-1

Source DB:  PubMed          Journal:  Test (Madr)        ISSN: 1133-0686            Impact factor:   2.345


  2 in total

1.  Better-than-chance classification for signal detection.

Authors:  Jonathan D Rosenblatt; Yuval Benjamini; Roee Gilron; Roy Mukamel; Jelle J Goeman
Journal:  Biostatistics       Date:  2021-04-10       Impact factor: 5.899

2.  Statistical quantification of confounding bias in machine learning models.

Authors:  Tamas Spisak
Journal:  Gigascience       Date:  2022-08-26       Impact factor: 7.658

  2 in total

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