Controlled Morris method: A new factor screening approach empowered by a distribution-free sequential multiple testing procedure

2019 ◽  
Vol 189 ◽  
pp. 299-314 ◽  
Author(s):  
Wen Shi ◽  
Xi Chen
Keyword(s):  
Multiple Testing ◽  
Testing Procedure ◽  
Multiple Testing Procedure ◽  
Factor Screening ◽  
Distribution Free ◽  
Morris Method ◽  
Screening Approach

scholarly journals A region-based multiple testing method for hypotheses ordered in space or time

2015 ◽  
Vol 14 (1) ◽  
pp. 1-19 ◽  
Author(s):  
Rosa J. Meijer ◽  
Thijmen J.P. Krebs ◽  
Jelle J. Goeman
Keyword(s):  
Error Rate ◽  
Null Hypothesis ◽  
Multiple Testing ◽  
Testing Procedure ◽  
Familywise Error Rate ◽  
Expected Number ◽  
Multiple Testing Procedure ◽  
Exact Location ◽  
Testing Method

AbstractWe present a multiple testing method for hypotheses that are ordered in space or time. Given such hypotheses, the elementary hypotheses as well as regions of consecutive hypotheses are of interest. These region hypotheses not only have intrinsic meaning but testing them also has the advantage that (potentially small) signals across a region are combined in one test. Because the expected number and length of potentially interesting regions are usually not available beforehand, we propose a method that tests all possible region hypotheses as well as all individual hypotheses in a single multiple testing procedure that controls the familywise error rate. We start at testing the global null-hypothesis and when this hypothesis can be rejected we continue with further specifying the exact location/locations of the effect present. The method is implemented in the

scholarly journals An Alpha-Exhaustive Multiple Testing Procedure

2016 ◽  
Vol 6 (2) ◽  
pp. 30-41
Author(s):  
Mark Chang ◽  
Xuan Deng ◽  
John Balser
Keyword(s):  
Multiple Testing ◽  
Testing Procedure ◽  
Multiple Testing Procedure

A factor-adjusted multiple testing procedure for ERP data analysis

2012 ◽  
Vol 44 (3) ◽  
pp. 635-643 ◽  
Author(s):  
David Causeur ◽  
Mei-Chen Chu ◽  
Shulan Hsieh ◽  
Ching-Fan Sheu
Keyword(s):  
Data Analysis ◽  
Multiple Testing ◽  
Testing Procedure ◽  
Multiple Testing Procedure

The Inheritance Procedure: Multiple Testing of Tree-structured Hypotheses

2012 ◽  
Vol 11 (1) ◽  
Author(s):  
Jelle J. Goeman ◽  
Livio Finos
Keyword(s):  
Multiple Testing ◽  
Error Control ◽  
Testing Procedure ◽  
Tree Structure ◽  
Graph Structure ◽  
Multiple Testing Procedure ◽  
Natural Way ◽  
Chromosome Level

Hypotheses tests in bioinformatics can often be set in a tree structure in a very natural way, e.g. when tests are performed at probe, gene, and chromosome level. Exploiting this graph structure in a multiple testing procedure may result in a gain in power or increased interpretability of the results.We present the inheritance procedure, a method of familywise error control for hypotheses structured in a tree. The method starts testing at the top of the tree, following up on those branches in which it finds significant results, and following up on leaf nodes in the neighborhood of those leaves. The method is a uniform improvement over a recently proposed method by Meinshausen. The inheritance procedure has been implemented in the globaltest package which is available on www.bioconductor.org.

scholarly journals Trends in the extremes of environments associated with severe United States thunderstorms

2020 ◽  
pp. 1-38
Author(s):  
Erwan Koch ◽  
Jonathan Kohy ◽  
Anthony C. Davison ◽  
Chiara Lepore ◽  
Michael K. Tippett
Keyword(s):  
United States ◽  
Multiple Testing ◽  
Extreme Values ◽  
Southern Oscillation ◽  
Convective Available Potential Energy ◽  
Testing Procedure ◽  
Value Theory ◽  
Large Area ◽  
Multiple Testing Procedure ◽  
Severe Thunderstorms

AbstractSevere thunderstorms can have devastating impacts. Concurrently high values of convective available potential energy (CAPE) and storm relative helicity (SRH) are known to be conducive to severe weather, so high values of have been used to indicate high risk of severe thunderstorms. We consider the extreme values of these three variables for a large area of the contiguous United States (US) over the period 1979–2015, and use extreme-value theory and a multiple testing procedure to show that there is a significant time trend in the extremes for PROD maxima in April, May and August, for CAPE maxima in April, May and June, and for maxima of SRH in April and May. These observed increases in CAPE are also relevant for rainfall extremes and are expected in a warmer climate, but have not previously been reported. Moreover, we show that the El Niño-Southern Oscillation explains variation in the extremes of PROD and SRH in February. Our results suggest that the risk from severe thunderstorms in April and May is increasing in parts of the US where it was already high, and that the risk from storms in February is increased over the main part of the region during La Niña years.

scholarly journals Paired test of matrix graphs and brain connectivity analysis

Biostatistics ◽  
2019 ◽  
Author(s):  
Yuting Ye ◽  
Yin Xia ◽  
Lexin Li
Keyword(s):  
Multiple Testing ◽  
Brain Connectivity ◽  
Estimation Error ◽  
Testing Procedure ◽  
Brain Regions ◽  
Brain Network ◽  
Test Statistic ◽  
Multiple Testing Procedure ◽  
Before And After ◽  
Graph Inference

Summary Inferring brain connectivity network and quantifying the significance of interactions between brain regions are of paramount importance in neuroscience. Although there have recently emerged some tests for graph inference based on independent samples, there is no readily available solution to test the change of brain network for paired and correlated samples. In this article, we develop a paired test of matrix graphs to infer brain connectivity network when the groups of samples are correlated. The proposed test statistic is both bias corrected and variance corrected, and achieves a small estimation error rate. The subsequent multiple testing procedure built on this test statistic is guaranteed to asymptotically control the false discovery rate at the pre-specified level. Both the methodology and theory of the new test are considerably different from the two independent samples framework, owing to the strong correlations of measurements on the same subjects before and after the stimulus activity. We illustrate the efficacy of our proposal through simulations and an analysis of an Alzheimer’s Disease Neuroimaging Initiative dataset.

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