Konietschke, Frank

[["dc.bibliographiccitation.journal","International Statistical Review"],["dc.contributor.affiliation","Konietschke, Frank; 2\r\nInstitute of Biometry and Clinical Epidemiology\r\nCharité\r\nBerlin Germany"],["dc.contributor.affiliation","Bathke, Arne C.; 3\r\nIntelligent Data Analytics (IDA) Lab\r\nSalzburg Austria"],["dc.contributor.affiliation","Pauly, Markus; 4\r\nFaculty of Statistics\r\nTU Dortmund University\r\nDortmund Germany"],["dc.contributor.author","Brunner, Edgar"],["dc.contributor.author","Konietschke, Frank"],["dc.contributor.author","Bathke, Arne C."],["dc.contributor.author","Pauly, Markus"],["dc.date.accessioned","2021-04-14T08:31:29Z"],["dc.date.available","2021-04-14T08:31:29Z"],["dc.date.issued","2020"],["dc.date.updated","2022-02-09T13:21:26Z"],["dc.description.abstract","Summary Rank‐based inference methods are applied in various disciplines, typically when procedures relying on standard normal theory are not justifiable. Various specific rank‐based methods have been developed for two and more samples and also for general factorial designs (e.g. Kruskal–Wallis test or Akritas–Arnold–Brunner test). It is the aim of the present paper (1) to demonstrate that traditional rank procedures for several samples or general factorial designs may lead to surprising results in case of unequal sample sizes as compared with equal sample sizes, (2) to explain why this is the case and (3) to provide a way to overcome these disadvantages. Theoretical investigations show that the surprising results can be explained by considering the non‐centralities of the test statistics, which may be non‐zero for the usual rank‐based procedures in case of unequal sample sizes, while they may be equal to 0 in case of equal sample sizes. A simple solution is to consider unweighted relative effects instead of weighted relative effects. The former effects are estimated by means of the so‐called pseudo‐ranks, while the usual ranks naturally lead to the latter effects. A real data example illustrates the practical meaning of the theoretical discussions."],["dc.description.sponsorship","Fonds zur Förderung der wissenschaftlichen Forschung http://dx.doi.org/10.13039/501100002428"],["dc.description.sponsorship","I 2697‐N31"],["dc.description.sponsorship","Deutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659"],["dc.identifier.doi","10.1111/insr.12418"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/83611"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-399"],["dc.relation.eissn","1751-5823"],["dc.relation.issn","0306-7734"],["dc.rights","This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes."],["dc.title","Ranks and Pseudo‐ranks—Surprising Results of Certain Rank Tests in Unbalanced Designs"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","1358"],["dc.bibliographiccitation.journal","Electronic Journal of Statistics"],["dc.bibliographiccitation.lastpage","1372"],["dc.bibliographiccitation.volume","6"],["dc.contributor.author","Konietschke, Frank"],["dc.contributor.author","Pauly, Markus"],["dc.date.accessioned","2018-11-07T09:15:06Z"],["dc.date.available","2018-11-07T09:15:06Z"],["dc.date.issued","2012"],["dc.description.abstract","We consider nonparametric ranking methods for matched pairs, whose distributions can have different shapes even under the null hypothesis of no treatment effect. Although the data may not be exchangeable under the null, we investigate a permutation approach as a valid procedure for finite sample sizes. In particular, we derive the limit of the studentized permutation distribution under alternatives, which can be used for the construction of (1 - alpha)-confidence intervals. Simulation studies show that the new approach is more accurate than its competitors. The procedures are illustrated using a real data set."],["dc.description.sponsorship","German Research Foundation [DFG-Br 655/16-1, HO 1687/9-1]"],["dc.identifier.doi","10.1214/12-EJS714"],["dc.identifier.isi","000306921000001"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/9503"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/27594"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Inst Mathematical Statistics"],["dc.relation.issn","1935-7524"],["dc.rights","CC BY 2.5"],["dc.rights.uri","https://creativecommons.org/licenses/by/2.5"],["dc.title","A studentized permutation test for the nonparametric Behrens-Fisher problem in paired data"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","283"],["dc.bibliographiccitation.issue","3"],["dc.bibliographiccitation.journal","Statistics and Computing"],["dc.bibliographiccitation.lastpage","296"],["dc.bibliographiccitation.volume","24"],["dc.contributor.author","Konietschke, Frank"],["dc.contributor.author","Pauly, Markus"],["dc.date.accessioned","2018-11-07T09:41:02Z"],["dc.date.available","2018-11-07T09:41:02Z"],["dc.date.issued","2014"],["dc.description.abstract","We study various bootstrap and permutation methods for matched pairs, whose distributions can have different shapes even under the null hypothesis of no treatment effect. Although the data may not be exchangeable under the null, we investigate different permutation approaches as valid procedures for finite sample sizes. It will be shown that permutation or bootstrap schemes, which neglect the dependency structure in the data, are asymptotically valid. Simulation studies show that these new tests improve the power of the t-test under non-normality."],["dc.description.sponsorship","German Research Foundation [DFG-Br 655/16-1, DFG-Ho 1687/9-1]"],["dc.identifier.doi","10.1007/s11222-012-9370-4"],["dc.identifier.isi","000334435400001"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/10241"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/33638"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Springer"],["dc.relation.issn","1573-1375"],["dc.relation.issn","0960-3174"],["dc.relation.orgunit","Wirtschaftswissenschaftliche Fakultät"],["dc.rights","Goescholar"],["dc.rights.uri","https://goescholar.uni-goettingen.de/licenses"],["dc.title","Bootstrapping and permuting paired t-test type statistics"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","616"],["dc.bibliographiccitation.issue","3"],["dc.bibliographiccitation.journal","Biometrical Journal"],["dc.bibliographiccitation.lastpage","629"],["dc.bibliographiccitation.volume","61"],["dc.contributor.author","Konietschke, Frank"],["dc.contributor.author","Friede, Tim"],["dc.contributor.author","Pauly, Markus"],["dc.date.accessioned","2019-07-09T11:51:23Z"],["dc.date.available","2019-07-09T11:51:23Z"],["dc.date.issued","2019"],["dc.description.abstract","Count data are common endpoints in clinical trials, for example magnetic resonance imaging lesion counts in multiple sclerosis. They often exhibit high levels of overdispersion, that is variances are larger than the means. Inference is regularly based on negative binomial regression along with maximum-likelihood estimators. Although this approach can account for heterogeneity it postulates a common overdispersion parameter across groups. Such parametric assumptions are usually difficult to verify, especially in small trials. Therefore, novel procedures that are based on asymptotic results for newly developed rate and variance estimators are proposed in a general framework. Moreover, in case of small samples the procedures are carried out using permutation techniques. Here, the usual assumption of exchangeability under the null hypothesis is not met due to varying follow-up times and unequal overdispersion parameters. This problem is solved by the use of studentized permutations leading to valid inference methods for situations with (i) varying follow-up times, (ii) different overdispersion parameters, and (iii) small sample sizes."],["dc.identifier.doi","10.1002/bimj.201800027"],["dc.identifier.pmid","30515878"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/16118"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/59940"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.relation","info:eu-repo/grantAgreement/EC/FP7/602144/EU//INSPIRE"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.subject.ddc","610"],["dc.title","Semi‐parametric analysis of overdispersed count and metric data with varying follow‐up times: Asymptotic theory and small sample approximations"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]