Brunner, Edgar

[["dc.bibliographiccitation.issue","Code Snippet 1"],["dc.bibliographiccitation.journal","Journal of Statistical Software"],["dc.bibliographiccitation.volume","95"],["dc.contributor.author","Happ, Martin"],["dc.contributor.author","Zimmermann, Georg"],["dc.contributor.author","Brunner, Edgar"],["dc.contributor.author","Bathke, Arne C."],["dc.date.accessioned","2021-04-14T08:32:41Z"],["dc.date.available","2021-04-14T08:32:41Z"],["dc.date.issued","2020"],["dc.identifier.doi","10.18637/jss.v095.c01"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/83986"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-399"],["dc.relation.eissn","1548-7660"],["dc.title","Pseudo-Ranks: How to Calculate Them Efficiently in R"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","363"],["dc.bibliographiccitation.issue","3"],["dc.bibliographiccitation.journal","Statistics in Medicine"],["dc.bibliographiccitation.lastpage","375"],["dc.bibliographiccitation.volume","38"],["dc.contributor.author","Happ, Martin"],["dc.contributor.author","Bathke, Arne C."],["dc.contributor.author","Brunner, Edgar"],["dc.date.accessioned","2020-12-10T14:07:12Z"],["dc.date.available","2020-12-10T14:07:12Z"],["dc.date.issued","2018"],["dc.identifier.doi","10.1002/sim.v38.3"],["dc.identifier.issn","0277-6715"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/70145"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.title","Optimal sample size planning for the Wilcoxon-Mann-Whitney test"],["dc.title.alternative","Optimal sample size planning for the Wilcoxon-Mann-Whitney test"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","1895"],["dc.bibliographiccitation.issue","8"],["dc.bibliographiccitation.journal","Computational Statistics & Data Analysis"],["dc.bibliographiccitation.lastpage","1905"],["dc.bibliographiccitation.volume","54"],["dc.contributor.author","Konietschke, Frank"],["dc.contributor.author","Bathke, Arne C."],["dc.contributor.author","Hothorn, Ludwig A."],["dc.contributor.author","Brunner, E."],["dc.date.accessioned","2018-11-07T08:40:41Z"],["dc.date.available","2018-11-07T08:40:41Z"],["dc.date.issued","2010"],["dc.description.abstract","The several sample case of the so-called nonparametric Behrens-Fisher problem in repeated measures designs is considered. That is, even under the null hypothesis, the marginal distribution functions in the different groups may have different shapes, and are not assumed to be equal. Moreover, the continuity of the marginal distribution functions is not required so that data with ties and, particularly, ordered categorical data are covered by this model. A multiple relative treatment effect is defined which can be estimated by using the mid-ranks of the observations within pairwise samples. The asymptotic distribution of this estimator is derived, along with a consistent estimator of its asymptotic covariance matrix. In addition, a multiple contrast test and related simultaneous confidence intervals for the relative marginal effects are derived and compared to rank-based Wald-type and ANOVA-type statistics. Simulations show that the ANOVA-type statistic and the multiple contrast test appear to maintain the pre-assigned level of the test quite accurately (even for rather small sample sizes) while the Wald-type statistic leads, as expected, to somewhat liberal decisions. Regarding the power, none of the statistics is uniformly superior. A real data set illustrates the application. (C) 2010 Elsevier B.V. All rights reserved."],["dc.identifier.doi","10.1016/j.csda.2010.02.019"],["dc.identifier.isi","000278155100001"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/19290"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Elsevier Science Bv"],["dc.relation.issn","0167-9473"],["dc.title","Testing and estimation of purely nonparametric effects in repeated measures designs"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","301"],["dc.bibliographiccitation.issue","3"],["dc.bibliographiccitation.journal","Biometrical Journal"],["dc.bibliographiccitation.lastpage","316"],["dc.bibliographiccitation.volume","54"],["dc.contributor.author","Brunner, Edgar"],["dc.contributor.author","Bathke, Arne C."],["dc.contributor.author","Placzek, Marius"],["dc.date.accessioned","2018-11-07T09:10:34Z"],["dc.date.available","2018-11-07T09:10:34Z"],["dc.date.issued","2012"],["dc.description.abstract","We present new inference methods for the analysis of low- and high-dimensional repeated measures data from two-sample designs that may be unbalanced, the number of repeated measures per subject may be larger than the number of subjects, covariance matrices are not assumed to be spherical, and they can differ between the two samples. In comparison, we demonstrate how crucial it is for the popular Huynh-Feldt (HF) method to make the restrictive and often unrealistic or unjustifiable assumption of equal covariance matrices. The new method is shown to maintain desired a-levels better than the well-known HF correction, as demonstrated in several simulation studies. The proposed test gains power when the number of repeated measures is increased in a manner that is consistent with the alternative. Thus, even increasing the number of measurements on the same subject may lead to an increase in power. Application of the new method is illustrated in detail, using two different real data sets. In one of them, the number of repeated measures per subject is smaller than the sample size, while in the other one, it is larger."],["dc.identifier.doi","10.1002/bimj.201100160"],["dc.identifier.isi","000305075600001"],["dc.identifier.pmid","22684999"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/26522"],["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","Estimation of Box's e for low- and high-dimensional repeated measures designs with unequal covariance matrices"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","1"],["dc.bibliographiccitation.journal","The American Statistician"],["dc.bibliographiccitation.lastpage","7"],["dc.contributor.author","Zimmermann, Georg"],["dc.contributor.author","Brunner, Edgar"],["dc.contributor.author","Brannath, Werner"],["dc.contributor.author","Happ, Martin"],["dc.contributor.author","Bathke, Arne C."],["dc.date.accessioned","2021-12-01T09:23:48Z"],["dc.date.available","2021-12-01T09:23:48Z"],["dc.date.issued","2021"],["dc.identifier.doi","10.1080/00031305.2021.1972836"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/94760"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-478"],["dc.relation.eissn","1537-2731"],["dc.relation.issn","0003-1305"],["dc.title","Pseudo-Ranks: The Better Way of Ranking?"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]