title: 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.
authors.affiliation: Research Center for Genes, Environment and Human Health, and Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan.
authors.forename: Ya-Hui
authors.initials: YH
authors.lastname: Yang
authors.affiliation: Research Center for Genes, Environment and Human Health, and Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan.
authors.forename: Wan-Yu
authors.initials: WY
authors.lastname: Lin
authors.affiliation: Research Center for Genes, Environment and Human Health, and Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan.
authors.forename: Wen-Chung
authors.initials: WC
authors.lastname: Lee
mesh_terms: D035843:Biomedical Research / Q000379:methods*; D005189:False Positive Reactions; D017143:Fuzzy Logic; D015233:Models, Statistical; D009010:Monte Carlo Method; D018401:Sample Size