Wagner, Thilo

[["dc.bibliographiccitation.firstpage","469"],["dc.bibliographiccitation.lastpage","499"],["dc.contributor.author","Dette, H."],["dc.contributor.author","Munk, Axel"],["dc.contributor.author","Wagner, Thilo"],["dc.contributor.editor","Gosh, S."],["dc.date.accessioned","2017-09-07T11:44:59Z"],["dc.date.available","2017-09-07T11:44:59Z"],["dc.date.issued","1998"],["dc.identifier.gro","3145444"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/3151"],["dc.language.iso","en"],["dc.notes.intern","lifescience"],["dc.notes.status","public"],["dc.notes.submitter","chake"],["dc.publisher","Dekker"],["dc.publisher.place","New York"],["dc.relation.ispartof","Multivariate Design and Sampling"],["dc.title","A review of variance estimators with extensions to multivariate nonparametric regression models"],["dc.type","book_chapter"],["dc.type.internalPublication","unknown"],["dc.type.peerReviewed","no"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","309"],["dc.bibliographiccitation.issue","3"],["dc.bibliographiccitation.journal","Journal of Nonparametric Statistics"],["dc.bibliographiccitation.lastpage","342"],["dc.bibliographiccitation.volume","12"],["dc.contributor.author","Dette, H."],["dc.contributor.author","Munk, Axel"],["dc.contributor.author","Wagner, Thilo"],["dc.date.accessioned","2017-09-07T11:47:25Z"],["dc.date.available","2017-09-07T11:47:25Z"],["dc.date.issued","2000"],["dc.description.abstract","In the multivariate nonparametric regression model Y = g(t)+ epsilon the problem of testing linearity of the regression function g and homoscedasticity of the distribution of the error epsilon is considered. For both problems a simple test is derived which is based on estimating the L-2-distance between the model space and the space induced by the hypothesis. The resulting statistics can be shown to be asymptotically normal, even under fixed alternatives. This extends and unifies recent results of Dette and Munk (1998a,b) to the multivariate case. A small simulation study on the finite sample behaviour of the proposed tests is reported and their properties are illustrated by analyzing a data example."],["dc.identifier.doi","10.1080/10485250008832811"],["dc.identifier.gro","3144425"],["dc.identifier.isi","000086789400001"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/2048"],["dc.notes.intern","WoS Import 2017-03-10"],["dc.notes.status","final"],["dc.notes.submitter","PUB_WoS_Import"],["dc.publisher","Gordon Breach Sci Publ Ltd"],["dc.relation.issn","1048-5252"],["dc.title","Testing model assumptions in multivariate linear regression models"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.subtype","original"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","751"],["dc.bibliographiccitation.journal","Journal of the Royal Statistical Society: Series B (Statistical Methodology)"],["dc.bibliographiccitation.lastpage","764"],["dc.bibliographiccitation.volume","60"],["dc.contributor.author","Dette, H."],["dc.contributor.author","Munk, Axel"],["dc.contributor.author","Wagner, Thilo"],["dc.date.accessioned","2017-09-07T11:48:13Z"],["dc.date.available","2017-09-07T11:48:13Z"],["dc.date.issued","1998"],["dc.description.abstract","The exact mean-squared error (MSE) of estimators of the variance in nonparametric regression based on quadratic forms is investigated. In particular, two classes of estimators are compared: Hall, Kay and Titterington's optimal difference-based estimators and a class of ordinary difference-based estimators which generalize methods proposed by Rice and Gasser, Sroka and Jennen-Steinmetz. For small sample sizes the MSE of the first estimator is essentially increased by the magnitude of the integrated first two squared derivatives of the regression function. It is shown that in many situations ordinary difference-based estimators are more appropriate for estimating the variance, because they control the bias much better and hence have a much better overall performance. It is also demonstrated that Rice's estimator does not always behave well. Data-driven guidelines are given to select the estimator with the smallest MSE."],["dc.identifier.doi","10.1111/1467-9868.00152"],["dc.identifier.gro","3144572"],["dc.identifier.isi","000075826600006"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/2210"],["dc.notes.intern","WoS Import 2017-03-10"],["dc.notes.status","final"],["dc.notes.submitter","PUB_WoS_Import"],["dc.publisher","Blackwell Publ Ltd"],["dc.relation.issn","1369-7412"],["dc.title","Estimating the variance in nonparametric regression - what is a reasonable choice?"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.subtype","original"],["dspace.entity.type","Publication"]]