title: Classes of Multiple Decision Functions Strongly Controlling FWER and FDR.
abstract: Two general classes of multiple decision functions, where each member of the first class strongly controls the family-wise error rate (FWER), while each member of the second class strongly controls the false discovery rate (FDR), are described. These classes offer the possibility that optimal multiple decision functions with respect to a pre-specified Type II error criterion, such as the missed discovery rate (MDR), could be found which control the FWER or FDR Type I error rates. The gain in MDR of the associated FDR-controlling procedure relative to the well-known Benjamini-Hochberg (BH) procedure is demonstrated via a modest simulation study with gamma-distributed component data. Such multiple decision functions may have the potential of being utilized in multiple testing, specifically in the analysis of high-dimensional data sets.
authors.affiliation: Department of Statistics, University of South Carolina, Columbia, SC 29208 USA, Tel.: 803-576-5813, , pena@stat.sc.edu.
authors.forename: Edsel A
authors.initials: EA
authors.lastname: Peña
authors.affiliation: Department of Statistics, Oklahoma State University, Stillwater, OK 74078, USA, jhabige@okstate.edu.
authors.forename: Joshua D
authors.initials: JD
authors.lastname: Habiger
authors.affiliation: Department of Mathematics and Statistics, Florida International University, Miami, FL 33199, USA, wenswu@fiu.edu.
authors.forename: Wensong
authors.initials: W
authors.lastname: Wu
keywords: false discovery rate; family-wise error rate; missed discovery rate; multiple decision problem; multiple testing; strong control