A Fuzzy Permutation Method for False Discovery Rate Control.

Abstract:

:Biomedical researchers often encounter the large-p-small-n situations-a great number of variables are measured/recorded for only a few subjects. The authors propose a fuzzy permutation method to address the multiple testing problem for small sample size studies. The method introduces fuzziness into standard permutation analysis to produce randomized p-values, which are then converted into q-values for false discovery rate controls. Simple algebra shows that the fuzzy permutation method is at least as powerful as the standard permutation method under any alternative. Monte-Carlo simulations show that the proposed method has desirable statistical properties whether the study variables are normally or non-normally distributed. A real dataset is analyzed to illustrate its use. The proposed fuzzy permutation method is recommended for use in the large-p-small-n settings.

journal_name

Sci Rep

journal_title

Scientific reports

authors

Yang YH,Lin WY,Lee WC

doi

10.1038/srep28507

subject

Has Abstract

pub_date

2016-06-22 00:00:00

pages

28507

issn

2045-2322

pii

srep28507

journal_volume

6

pub_type

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