Optimal and Maximin Procedures for Multiple Testing Problems

Multiple Testing Procedures

2 Methods 5 2.1 Multiple hypothesis testing framework . . . . . . . . . . . . . 5 2.2 Test statistics null distribution . . . . . . . . . . . . . . . . . 11 2.3 Single-step procedures for control of general Type I error rates θ(FVn) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 2.4 Step-down procedures for control of the family-wise error rate 16 2.5 Augmentation multiple tes...

Bayesian multiple testing procedures for hotspot identification.

Ranking a group of candidate sites and selecting from it the high-risk locations or hotspots for detailed engineering study and countermeasure evaluation is the first step in a transport safety improvement program. Past studies have however mainly focused on the task of applying appropriate methods for ranking locations, with few focusing on the issue of how to define selection methods or thres...

Self-consistent multiple testing procedures

We study the control of the false discovery rate (FDR) for a general class of multiple testing procedures. We introduce a general condition, called “self-consistency”, on the set of hypotheses rejected by the procedure, which we show is sufficient to ensure the control of the corresponding false discovery rate under various conditions on the distribution of the pvalues. Maximizing the size of t...

Maximin Optimal Designs for the Compartmental Model

Multiple Testing Procedures and Applications to Genomics

The Bioconductor R package multtest implements widely applicable resampling-based single-step and stepwise multiple testing procedures (MTP) for controlling a broad class of Type I error rates, in testing problems involving general data generating distributions (with arbitrary dependence structures among variables), null hypotheses, and test statistics [1, 2, 3, 4, 5]. The current version of mu...