Nonparametric relevance-shifted multiple testing procedures for the analysis of high-dimensional multivariate data with small sample sizes

Downloadstatistik des Dokuments (Auswertung nach COUNTER):

Frömke, C.; Hothorn, Ludwig, A.; Kropf, S.: Nonparametric relevance-shifted multiple testing procedures for the analysis of high-dimensional multivariate data with small sample sizes. In: BMC Bioinformatics 9 (2008), 54. DOI. http://dx.doi.org/10.1186/1471-2105-9-54

Version im Repositorium

Zum Zitieren der Version im Repositorium verwenden Sie bitte diesen DOI: https://doi.org/10.15488/629

Zeitraum, für den die Download-Zahlen angezeigt werden:

Jahr: 
Monat: 

Summe der Downloads: 58




Kleine Vorschau
Zusammenfassung: 
Background: In many research areas it is necessary to find differences between treatment groups with several variables. For example, studies of microarray data seek to find a significant difference in location parameters from zero or one for ratios thereof for each variable. However, in some studies a significant deviation of the difference in locations from zero (or 1 in terms of the ratio) is biologically meaningless. A relevant difference or ratio is sought in such cases. Results: This article addresses the use of relevance-shifted tests on ratios for a multivariate parallel two-sample group design. Two empirical procedures are proposed which embed the relevance-shifted test on ratios. As both procedures test a hypothesis for each variable, the resulting multiple testing problem has to be considered. Hence, the procedures include a multiplicity correction. Both procedures are extensions of available procedures for point null hypotheses achieving exact control of the familywise error rate. Whereas the shift of the null hypothesis alone would give straight-forward solutions, the problems that are the reason for the empirical considerations discussed here arise by the fact that the shift is considered in both directions and the whole parameter space in between these two limits has to be accepted as null hypothesis. Conclusion: The first algorithm to be discussed uses a permutation algorithm, and is appropriate for designs with a moderately large number of observations. However, many experiments have limited sample sizes. Then the second procedure might be more appropriate, where multiplicity is corrected according to a concept of data-driven order of hypotheses.
Lizenzbestimmungen: CC BY 2.0
Publikationstyp: article
Publikationsstatus: publishedVersion
Erstveröffentlichung: 2008
Die Publikation erscheint in Sammlung(en):Naturwissenschaftliche Fakultät

Verteilung der Downloads über den gewählten Zeitraum:

Herkunft der Downloads nach Ländern:

Pos. Land Downloads
Anzahl Proz.
1 image of flag of Germany Germany 54 93,10%
2 image of flag of China China 2 3,45%
3 image of flag of Russian Federation Russian Federation 1 1,72%
4 image of flag of Switzerland Switzerland 1 1,72%

Weitere Download-Zahlen und Ranglisten:


Hinweis

Zur Erhebung der Downloadstatistiken kommen entsprechend dem „COUNTER Code of Practice for e-Resources“ international anerkannte Regeln und Normen zur Anwendung. COUNTER ist eine internationale Non-Profit-Organisation, in der Bibliotheksverbände, Datenbankanbieter und Verlage gemeinsam an Standards zur Erhebung, Speicherung und Verarbeitung von Nutzungsdaten elektronischer Ressourcen arbeiten, welche so Objektivität und Vergleichbarkeit gewährleisten sollen. Es werden hierbei ausschließlich Zugriffe auf die entsprechenden Volltexte ausgewertet, keine Aufrufe der Website an sich.

Suche im Repositorium


Durchblättern