Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Multivariate multiple test procedures based on nonparametric copula estimation
Stockholm University, Faculty of Science, Department of Mathematics.ORCID iD: 0000-0001-7855-8221
2019 (English)In: Biometrical Journal, ISSN 0323-3847, E-ISSN 1521-4036, Vol. 61, no 1, p. 40-61Article in journal (Refereed) Published
Abstract [en]

Multivariate multiple test procedures have received growing attention recently. This is due to the fact that data generated by modern applications typically are highdimensional, but possess pronounced dependencies due to the technical mechanisms involved in the experiments. Hence, it is possible and often necessary to exploit these dependencies in order to achieve reasonable power. In the present paper, we express dependency structures in the most general manner, namely, by means of copula functions. One class of nonparametric copula estimators is constituted by Bernstein copulae. We extend previous statistical results regarding bivariate Bernstein copulae to the multivariate case and study their impact on multiple tests. In particular, we utilize them to derive asymptotic confidence regions for the family-wise error rate (FWER) of multiple test procedures that are empirically calibrated by making use of Bernstein copulae approximations of the dependency structure among the test statistics. This extends a similar approach by Stange et al. (2015) in the parametric case. A simulation study quantifies the gain in FWER level exhaustion and, consequently, power that can be achieved by exploiting the dependencies, in comparison with common threshold calibrations like the Bonferroni or Šidák corrections. Finally, we demonstrate an application of the proposed methodology to real-life data from insurance.

Place, publisher, year, edition, pages
2019. Vol. 61, no 1, p. 40-61
Keywords [en]
asymptotic oscillation behavior, family-wise error rate, p-value, risk management
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:su:diva-164901DOI: 10.1002/bimj.201700205ISI: 000455537800004OAI: oai:DiVA.org:su-164901DiVA, id: diva2:1280654
Conference
10th International Conference on Multiple Comparison Procedures (MCP), Riverside, CA, USA, June 20-23, 2017
Available from: 2019-01-20 Created: 2019-01-20 Last updated: 2019-02-04Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full text

Search in DiVA

By author/editor
Bodnar, Taras
By organisation
Department of Mathematics
In the same journal
Biometrical Journal
Probability Theory and Statistics

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 90 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf