Konietschke, Frank

[["dc.bibliographiccitation.firstpage","63"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","The International Journal of Biostatistics"],["dc.bibliographiccitation.lastpage","73"],["dc.bibliographiccitation.volume","9"],["dc.contributor.author","Konietschke, Frank"],["dc.contributor.author","Boesiger, Sandra"],["dc.contributor.author","Brunner, Edgar"],["dc.contributor.author","Hothorn, Ludwig A."],["dc.date.accessioned","2018-11-07T09:24:47Z"],["dc.date.available","2018-11-07T09:24:47Z"],["dc.date.issued","2013"],["dc.description.abstract","Multiple contrast tests can be used to test arbitrary linear hypotheses by providing local and global test decisions as well as simultaneous confidence intervals. The ANOVA-F-test on the contrary can be used to test the global null hypothesis of no treatment effect. Thus, multiple contrast tests provide more information than the analysis of variance (ANOVA) by offering which levels cause the significance. We compare the exact powers of the ANOVA-F-test and multiple contrast tests to reject the global null hypothesis. Hereby, we compute their least favorable configurations (LFCs). It turns out that both procedures have the same LFCs under certain conditions. Exact power investigations show that their powers are equal to detect their LFCs."],["dc.description.sponsorship","German Research Foundation [DFG-Br 655/16-1, Ho 1687/9-1]"],["dc.identifier.doi","10.1515/ijb-2012-0020"],["dc.identifier.isi","000329433300005"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/29910"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Walter De Gruyter Gmbh"],["dc.relation.issn","1557-4679"],["dc.relation.issn","2194-573X"],["dc.title","Are Multiple Contrast Tests Superior to the ANOVA?"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["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","14"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Statistics in Biopharmaceutical Research"],["dc.bibliographiccitation.lastpage","27"],["dc.bibliographiccitation.volume","4"],["dc.contributor.author","Konietschke, Frank"],["dc.contributor.author","Hothorn, Ludwig A."],["dc.date.accessioned","2018-11-07T09:15:03Z"],["dc.date.available","2018-11-07T09:15:03Z"],["dc.date.issued","2012"],["dc.description.abstract","The U.S. National Toxicology Program uses Shirley's test for the evaluation of endpoints in hematology, clinical chemistry, and urinalysis, which are assumed to be skewed distributed. Until now, no algorithm for Shirley's test for general unbalanced designs has been devised. A Shirley-type procedure is provided for estimating multiplicity-adjusted p-values or simultaneous confidence intervals for any continuous or discrete distributions and possible heterogeneous variances. Routine evaluation can be performed using the open-source R package nparcomp. The use of simultaneous confidence intervals for the relative effect is recommended, since these scale-invariant intervals allow one to claim the statistical significance of multiple endpoints as well as their biological relevance."],["dc.description.sponsorship","German Science Foundation [Br-655/16-1, HO 1687/9-1]"],["dc.identifier.doi","10.1080/19466315.2011.633861"],["dc.identifier.isi","000307662900002"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/27581"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Amer Statistical Assoc"],["dc.relation.issn","1946-6315"],["dc.title","Evaluation of Toxicological Studies Using a Nonparametric Shirley-Type Trend Test for Comparing Several Dose Levels with a Control Group"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]