Konietschke, Frank

[["dc.bibliographiccitation.firstpage","461"],["dc.bibliographiccitation.issue","2"],["dc.bibliographiccitation.journal","JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY"],["dc.bibliographiccitation.lastpage","473"],["dc.bibliographiccitation.volume","77"],["dc.contributor.author","Pauly, Markus"],["dc.contributor.author","Brunner, Edgar"],["dc.contributor.author","Konietschke, Frank"],["dc.date.accessioned","2018-11-07T10:00:44Z"],["dc.date.available","2018-11-07T10:00:44Z"],["dc.date.issued","2015"],["dc.description.abstract","In general factorial designs where no homoscedasticity or a particular error distribution is assumed, the well-known Wald-type statistic is a simple asymptotically valid procedure. However, it is well known that it suffers from a poor finite sample approximation since the convergence to its (2) limit distribution is quite slow. This becomes even worse with an increasing number of factor levels. The aim of the paper is to improve the small sample behaviour of the Wald-type statistic, maintaining its applicability to general settings as crossed or hierarchically nested designs by applying a modified permutation approach. In particular, it is shown that this approach approximates the null distribution of the Wald-type statistic not only under the null hypothesis but also under the alternative yielding an asymptotically valid permutation test which is even finitely exact under exchangeability. Finally, its small sample behaviour is compared with competing procedures in an extensive simulation study."],["dc.description.sponsorship","German Research Foundation [DFG-Br 655/16-1, Ho 1687/9-1]"],["dc.identifier.doi","10.1111/rssb.12073"],["dc.identifier.isi","000349206900007"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/37872"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Wiley-blackwell"],["dc.relation.issn","1467-9868"],["dc.relation.issn","1369-7412"],["dc.title","Asymptotic permutation tests in general factorial designs"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","1319"],["dc.bibliographiccitation.issue","6"],["dc.bibliographiccitation.journal","Biometrical Journal"],["dc.bibliographiccitation.lastpage","1337"],["dc.bibliographiccitation.volume","58"],["dc.contributor.author","Pauly, Markus"],["dc.contributor.author","Asendorf, Thomas"],["dc.contributor.author","Konietschke, Frank"],["dc.date.accessioned","2018-11-07T10:06:24Z"],["dc.date.available","2018-11-07T10:06:24Z"],["dc.date.issued","2016"],["dc.description.abstract","We investigate rank-based studentized permutation methods for the nonparametric Behrens-Fisher problem, that is, inference methods for the area under the ROC curve. We hereby prove that the studentized permutation distribution of the Brunner-Munzel rank statistic is asymptotically standard normal, even under the alternative. Thus, incidentally providing the hitherto missing theoretical foundation for the Neubert and Brunner studentized permutation test. In particular, we do not only show its consistency, but also that confidence intervals for the underlying treatment effects can be computed by inverting this permutation test. In addition, we derive permutation-based range-preserving confidence intervals. Extensive simulation studies show that the permutation-based confidence intervals appear to maintain the preassigned coverage probability quite accurately (even for rather small sample sizes). For a convenient application of the proposed methods, a freely available software package for the statistical software R has been developed. A real data example illustrates the application."],["dc.description.sponsorship","German Research Foundation [DFG Br 655/16-1, Ho 1687/9-1, Pa 2409/3-1]"],["dc.identifier.doi","10.1002/bimj.201500105"],["dc.identifier.isi","000387148100004"],["dc.identifier.pmid","27502845"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/39086"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Wiley-blackwell"],["dc.relation.issn","1521-4036"],["dc.relation.issn","0323-3847"],["dc.title","Permutation-based inference for the AUC: A unified approach for continuous and discontinuous data"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","176"],["dc.bibliographiccitation.journal","Journal of Multivariate Analysis"],["dc.bibliographiccitation.lastpage","192"],["dc.bibliographiccitation.volume","171"],["dc.contributor.author","Umlauft, Maria"],["dc.contributor.author","Placzek, Marius"],["dc.contributor.author","Konietschke, Frank"],["dc.contributor.author","Pauly, Markus"],["dc.date.accessioned","2020-12-10T14:25:14Z"],["dc.date.available","2020-12-10T14:25:14Z"],["dc.date.issued","2019"],["dc.identifier.doi","10.1016/j.jmva.2018.12.005"],["dc.identifier.issn","0047-259X"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/72493"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.title","Wild bootstrapping rank-based procedures: Multiple testing in nonparametric factorial repeated measures designs"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]