title: A conservative test for multiple comparison based on highly correlated test statistics.
abstract: In genetics, we often encounter a large number of highly correlated test statistics. The most famous conservative bound for multiple comparison is Bonferroni's bound, which is suitable when the test statistics are independent but not when the test statistics are highly correlated. This article proposes a new conservative bound that is easily calculated without multiple integration and is a good approximation when the test statistics are highly correlated. The performance of the proposed method is evaluated by simulation and real data analysis.
authors.affiliation: Graduate School of Mathematics, Kyushu University, 6-10-1 Hakozaki, Higashi-ku, Fukuoka 812-8581, Japan. nino@math.kyushu-u.ac.jp
authors.forename: Yoshiyuki
authors.initials: Y
authors.lastname: Ninomiya
authors.forename: Hironori
authors.initials: H
authors.lastname: Fujisawa
mesh_terms: D000465:Algorithms; D000818:Animals; D002874:Chromosome Mapping / Q000379:methods*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D051379:Mice; D008957:Models, Genetic; D015233:Models, Statistical; D020641:Polymorphism, Single Nucleotide / Q000235:genetics*; D040641:Quantitative Trait Loci / Q000235:genetics*; D013223:Statistics as Topic