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

Meijer Rosa J. (), Krebs Thijmen J.P. and Goeman Jelle J.
Additional contact information
Meijer Rosa J.: Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, Postzone S5-P, P.O. Box 9604, 2300 RC Leiden, The Netherlands
Krebs Thijmen J.P.: Faculty of Electrical Engineering, Mathematics and Information Technology, Delft University, P.O. Box 5031, 2600 GA Delft, The Netherlands
Goeman Jelle J.: Section Biostatistics, Department for Health Evidence, Radboud University Medical Center, Postzone 133, P.O. Box 9101, 6500 HB Nijmegen, The Netherlands

Statistical Applications in Genetics and Molecular Biology, 2015, vol. 14, issue 1, 1-19

Abstract: We 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 R package cherry and is illustrated on a DNA copy number data set.

Keywords: familywise error rate; multiple testing; region hypotheses (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://doi.org/10.1515/sagmb-2013-0075 (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bpj:sagmbi:v:14:y:2015:i:1:p:1-19:n:1

Ordering information: This journal article can be ordered from
https://www.degruyter.com/journal/key/sagmb/html

DOI: 10.1515/sagmb-2013-0075

Access Statistics for this article

Statistical Applications in Genetics and Molecular Biology is currently edited by Michael P. H. Stumpf

More articles in Statistical Applications in Genetics and Molecular Biology from De Gruyter
Bibliographic data for series maintained by Peter Golla ().