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.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","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"]]

[["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"]]