Spiegel, Elmar

[["dc.bibliographiccitation.firstpage","2126"],["dc.bibliographiccitation.issue","8"],["dc.bibliographiccitation.journal","Leukemia"],["dc.bibliographiccitation.lastpage","2126"],["dc.bibliographiccitation.volume","33"],["dc.contributor.author","Vrzalikova, K"],["dc.contributor.author","Ibrahim, M"],["dc.contributor.author","Vockerodt, M"],["dc.contributor.author","Perry, T"],["dc.contributor.author","Margielewska, S"],["dc.contributor.author","Lupino, L"],["dc.contributor.author","Nagy, E"],["dc.contributor.author","Soilleux, E"],["dc.contributor.author","Liebelt, D"],["dc.contributor.author","Hollows, R"],["dc.contributor.author","Last, A"],["dc.contributor.author","Reynolds, G"],["dc.contributor.author","Abdullah, M"],["dc.contributor.author","Curley, H"],["dc.contributor.author","Care, M"],["dc.contributor.author","Krappmann, D"],["dc.contributor.author","Tooze, R"],["dc.contributor.author","Allegood, J"],["dc.contributor.author","Spiegel, S"],["dc.contributor.author","Wei, W"],["dc.contributor.author","Woodman, C B J"],["dc.contributor.author","Murray, P G"],["dc.date.accessioned","2020-12-10T18:09:34Z"],["dc.date.available","2020-12-10T18:09:34Z"],["dc.date.issued","2019"],["dc.identifier.doi","10.1038/s41375-019-0511-z"],["dc.identifier.eissn","1476-5551"],["dc.identifier.issn","0887-6924"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/16569"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/73696"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.notes.intern","Merged from goescholar"],["dc.relation.iserratumof","/handle/2/73661"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","Correction: S1PR1 drives a feedforward signalling loop to regulate BATF3 and the transcriptional programme of Hodgkin lymphoma cells"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","erratum_ja"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","1471082X1982994"],["dc.bibliographiccitation.firstpage","386"],["dc.bibliographiccitation.issue","4"],["dc.bibliographiccitation.journal","Statistical Modelling"],["dc.bibliographiccitation.lastpage","409"],["dc.bibliographiccitation.volume","20"],["dc.contributor.author","Kneib, Thomas"],["dc.contributor.author","Otto-Sobotka, Fabian"],["dc.contributor.author","Spiegel, Elmar"],["dc.date.accessioned","2020-04-06T09:04:47Z"],["dc.date.available","2020-04-06T09:04:47Z"],["dc.date.issued","2020-08"],["dc.description.abstract","Spatio-temporal models are becoming increasingly popular in recent regression research. However, they usually rely on the assumption of a specific parametric distribution for the response and/or homoscedastic error terms. In this article, we propose to apply semiparametric expectile regression to model spatio-temporal effects beyond the mean. Besides the removal of the assumption of a specific distribution and homoscedasticity, with expectile regression the whole distribution of the response can be estimated. For the use of expectiles, we interpret them as weighted means and estimate them by established tools of (penalized) least squares regression. The spatio-temporal effect is set up as an interaction between time and space either based on trivariate tensor product P-splines or the tensor product of a Gaussian Markov random field and a univariate P-spline. Importantly, the model can easily be split up into main effects and interactions to facilitate interpretation. The method is presented along the analysis of spatio-temporal variation of temperatures in Germany from 1980 to 2014."],["dc.identifier.doi","10.1177/1471082X19829945"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/63825"],["dc.language.iso","en"],["dc.notes.intern","DeepGreen Import"],["dc.publisher","SAGE Publications"],["dc.relation.eissn","1477-0342"],["dc.relation.issn","1471-082X"],["dc.relation.issn","1477-0342"],["dc.title","Spatio-temporal expectile regression models"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","3008"],["dc.bibliographiccitation.issue","2"],["dc.bibliographiccitation.journal","Electronic Journal of Statistics"],["dc.bibliographiccitation.lastpage","3038"],["dc.bibliographiccitation.volume","11"],["dc.contributor.author","Spiegel, Elmar"],["dc.contributor.author","Sobotka, Fabian"],["dc.contributor.author","Kneib, Thomas"],["dc.date.accessioned","2020-12-10T18:41:48Z"],["dc.date.available","2020-12-10T18:41:48Z"],["dc.date.issued","2017"],["dc.description.sponsorship","Open-Access-Publikationsfonds 2017"],["dc.identifier.doi","10.1214/17-EJS1307"],["dc.identifier.eissn","1935-7524"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/15226"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/77682"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","Model selection in semiparametric expectile regression"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]