We present a novel method for controlling the k-familywise error rate (k-FWER) in the linear regression setting using the knockoffs framework first introduced by Barber and Candès. Our procedure, which we also refer to as knockoffs, can be applied with any design matrix with at least as many observations as variables, and does not require knowing the noise variance. Unlike other multiple testin...

There have been many discussions of how Type I errors should be controlled when many hypotheses are tested (e.g., all possible comparisons of means, correlations, proportions, the coefficients in hierarchical models, etc.). By and large, researchers have adopted familywise (FWER) control, though this practice certainly is not universal. Familywise control is intended to deal with the multiplici...

Frane (2015) pointed out the difference between per-family and familywise Type I error control and how different multiple comparison procedures control one method but not necessarily the other. He then went on to demonstrate in the context of a two group multivariate design containing different numbers of dependent variables and correlations between variables how the per-family rate inflates be...

In high dimensional data analysis (such as gene expression, spatial epidemiology, or brain imaging studies), we often test thousands or more hypotheses simultaneously. As the number of tests increases, the chance of observing some statistically significant tests is very high even when all null hypotheses are true. Consequently, we could reach incorrect conclusions regarding the hypotheses. Rese...

In light of the recent experimental results on decays B+ → fX(1300)K and B±(0) → J/ψK X (1430) from Belle, we study the B → P (V )S type decays, B±(0) → f0(1370)K and B±(0) → J/ψK 0 (1430), in comparison with the B → P (V )T type decays, B±(0) → f2(1270)K and B±(0) → J/ψK 2 (1430). We calculate the BRs for these decays by using the form factors obtained in the ISGW2 model [the improved version ...

The present article proposes two step-down multiple testing procedures for asymptotic control of the family-wise error rate (FWER): the first procedure is based on maxima of test statistics (step-down maxT), while the second relies on minima of unadjusted p-values (step-down minP). A key feature of our approach is the characterization and construction of a test statistics null distribution (rat...

Flexible designs offer a large amount of flexibility in clinical trials with control of the type I error rate. This allows the combination of trials from different clinical phases of a drug development process. Such combinations require designs where hypotheses are selected and/or added at interim analysis without knowing the selection rule in advance so that both flexibility and multiplicity i...

In this paper we present a new R package called sgof for multiple hypothesis testing. The principal aim of this package is to implement SGoF-type multiple testing methods, known to be more powerful than the classical false discovery rate (FDR) and family-wise error rate (FWER) based methods in certain situations, particularly when the number of tests is large. This package includes Binomial and...

Charmed meson decays into a pseudoscalar meson P and a tensor meson T are studied. The charm to tensor meson transition form factors are evaluated in the Isgur-Scora-Grinstein-Wise (ISGW) quark model. It is shown that the Cabibbo-allowed decay D+ s → f2(1270)π is dominated by the W -annihilation contribution and has the largest branching ratio in D → TP decays. We argue that the Cabibbo-suppres...

1 Overview The multtest package contains a collection of functions for multiple hypothesis testing. These functions can be used to identify differentially expressed genes in microarray experiments, i.e., genes whose expression levels are associated with a response or covariate of interest. Introduction to multiple testing. This document provides a tutorial for using the multtest package. For a ...