Kneib, Thomas

[["dc.contributor.author","Kneib, Thomas"],["dc.date.accessioned","2020-04-03T14:48:54Z"],["dc.date.available","2020-04-03T14:48:54Z"],["dc.date.issued","2004"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/63695"],["dc.title","Modelling geoadditive survival data"],["dc.type","preprint"],["dc.type.internalPublication","unknown"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","1"],["dc.bibliographiccitation.issue","S1"],["dc.bibliographiccitation.journal","Biometrical Journal"],["dc.bibliographiccitation.lastpage","1"],["dc.bibliographiccitation.volume","46"],["dc.contributor.author","Kneib, Thomas"],["dc.date.accessioned","2020-04-06T06:31:38Z"],["dc.date.available","2020-04-06T06:31:38Z"],["dc.date.issued","2004"],["dc.identifier.doi","10.1002/bimj.200490310"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/63737"],["dc.relation.issn","0323-3847"],["dc.relation.issn","1521-4036"],["dc.title","Penalized structured additive regression for space-time data"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","203"],["dc.bibliographiccitation.issue","2"],["dc.bibliographiccitation.journal","Statistics and Computing"],["dc.bibliographiccitation.lastpage","219"],["dc.bibliographiccitation.volume","20"],["dc.contributor.author","Fahrmeir, Ludwig"],["dc.contributor.author","Kneib, Thomas"],["dc.contributor.author","Konrath, Susanne"],["dc.date.accessioned","2017-09-07T11:47:17Z"],["dc.date.available","2017-09-07T11:47:17Z"],["dc.date.issued","2009"],["dc.identifier.doi","10.1007/s11222-009-9158-3"],["dc.identifier.gro","3149316"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/5979"],["dc.notes.intern","Kneib Crossref Import"],["dc.notes.status","public"],["dc.notes.submitter","chake"],["dc.publisher","Springer Nature"],["dc.relation.issn","0960-3174"],["dc.title","Bayesian regularisation in structured additive regression: a unifying perspective on shrinkage, smoothing and predictor selection"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dc.type.peerReviewed","no"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","25"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Journal of the Royal Statistical Society. Series C, Applied statistics"],["dc.bibliographiccitation.lastpage","45"],["dc.bibliographiccitation.volume","62"],["dc.contributor.author","Sobotka, Fabian"],["dc.contributor.author","Radice, Rosalba"],["dc.contributor.author","Marra, Giampiero"],["dc.contributor.author","Kneib, Thomas"],["dc.date.accessioned","2017-09-07T11:47:18Z"],["dc.date.available","2017-09-07T11:47:18Z"],["dc.date.issued","2013"],["dc.identifier.doi","10.1111/j.1467-9876.2012.01050.x"],["dc.identifier.gro","3149314"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/5977"],["dc.notes.intern","Kneib Crossref Import"],["dc.notes.status","public"],["dc.notes.submitter","chake"],["dc.publisher","Wiley-Blackwell"],["dc.relation.issn","0035-9254"],["dc.title","Estimating the relationship between women's education and fertility in Botswana by using an instrumental variable approach to semiparametric expectile regression"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dc.type.peerReviewed","no"],["dspace.entity.type","Publication"]]

[["dc.contributor.author","Langrock, Roland"],["dc.contributor.author","Michelot, Théo"],["dc.contributor.author","Sohn, Alexander"],["dc.contributor.author","Kneib, Thomas"],["dc.date.accessioned","2020-04-03T13:30:36Z"],["dc.date.available","2020-04-03T13:30:36Z"],["dc.date.issued","2013-08-27"],["dc.description.abstract","Stochastic volatility (SV) models mimic many of the stylized facts attributed to time series of asset returns, while maintaining conceptual simplicity. The commonly made assumption of conditionally normally distributed or Student-t-distributed returns, given the volatility, has however been questioned. In this manuscript, we introduce a novel maximum penalized likelihood approach for estimating the conditional distribution in an SV model in a nonparametric way, thus avoiding any potentially critical assumptions on the shape. The considered framework exploits the strengths both of the powerful hidden Markov model machinery and of penalized B-splines, and constitutes a powerful and flexible alternative to recently developed Bayesian approaches to semiparametric SV modelling. We demonstrate the feasibility of the approach in a simulation study before outlining its potential in applications to three series of returns on stocks and one series of stock index returns."],["dc.identifier.arxiv","1308.5836v3"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/63632"],["dc.title","Semiparametric stochastic volatility modelling using penalized splines"],["dc.type","preprint"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","634"],["dc.bibliographiccitation.issue","6"],["dc.bibliographiccitation.journal","Shock"],["dc.bibliographiccitation.lastpage","641"],["dc.bibliographiccitation.volume","30"],["dc.contributor.author","Moubarak, Patricia"],["dc.contributor.author","Zilker, Susanne"],["dc.contributor.author","Wolf, Hilde"],["dc.contributor.author","Hofner, Benjamin"],["dc.contributor.author","Kneib, Thomas"],["dc.contributor.author","Küchenhoff, Helmut"],["dc.contributor.author","Jauch, Karl-Walter"],["dc.contributor.author","Hartl, Wolfgang H"],["dc.date.accessioned","2020-04-06T08:37:29Z"],["dc.date.available","2020-04-06T08:37:29Z"],["dc.date.issued","2008-12"],["dc.description.abstract","Recent controlled studies that evaluated the efficacy of an adjuvant antithrombin (AT) III therapy in severe sepsis used a uniform AT-III dose and duration of therapy and did not adjust to the actual AT-III deficit. It was the aim of the present study to explore if surgical patients with severe sepsis might have a treatment benefit from an activity-guided AT-III therapy. We performed a retrospective cohort analysis using an intensive care unit (ICU) database. To examine the effect of AT-III on outcome and on red cell transfusion rate, multivariate generalized additive models (GAMs), Cox-type additive hazard regression models, and propensity score adjustments were used. Five hundred forty-five postoperative surgical patients requiring ICU therapy because of severe sepsis were analyzed. Antithrombin III was given to those patients believed to be at a high risk of dying. Antithrombin III therapy was guided by the individual AT-III activity and aimed at the maintenance of an activity of at least 100%. Antithrombin III supplementation was discontinued after the plasma AT-III activity had been persistently normal without simultaneous AT-III infusion. We found that patients receiving additional AT-III (n = 230) were sicker than those on standard therapy (n = 315; admission Acute physiology and chronic health evaluation II score, 19.8 +/- 7.3 vs. 17.9 +/- 7.1 [mean +/- SD]; P < 0.005). Correspondingly, 28-day mortality was higher in patients who had an additional AT-III therapy than in those on standard therapy (46.3% vs. 36.9%; P < 0.03), as was the number of red cell units transfused during ICU stay (21.5 +/- 26.7 vs. 9.3 +/- 12.1; P < 0.001). At multivariate analysis, there was no significant effect of AT-III therapy on 28-day mortality (GAM: odds ratio, 1.012; 95% confidence interval [CI], 0.651 - 1.573; P = 0.957) and 90-day survival time (Cox-type additive hazard regression: hazard ratio, 1.034; 95% CI, 0.779 - 1.387; P = 0.794). However, AT-III therapy was associated with a significantly higher frequency of red cell unit transfusion (GAM/zero-inflated Poisson: estimate, 1.26; 95% CI, 1.15 - 139; P < 0.001). Our results suggest that there seems to be no relevant effect of an activity-guided AT-III therapy on the prognosis of surgical patients with severe sepsis. However, transfusion frequency rises by AT-III therapy."],["dc.identifier.doi","10.1097/SHK.0b013e31817d3e14"],["dc.identifier.pmid","18520701"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/63821"],["dc.language.iso","en"],["dc.relation.eissn","1540-0514"],["dc.relation.issn","1073-2322"],["dc.title","Activity-guided antithrombin III therapy in severe surgical sepsis: efficacy and safety according to a retrospective data analysis"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","433"],["dc.bibliographiccitation.issue","5"],["dc.bibliographiccitation.journal","Statistical Modelling"],["dc.bibliographiccitation.lastpage","456"],["dc.bibliographiccitation.volume","15"],["dc.contributor.author","Waltrup, Linda Schulze"],["dc.contributor.author","Sobotka, Fabian"],["dc.contributor.author","Kneib, Thomas"],["dc.contributor.author","Kauermann, Göran"],["dc.date.accessioned","2018-11-07T09:50:45Z"],["dc.date.available","2018-11-07T09:50:45Z"],["dc.date.issued","2015"],["dc.description.abstract","Recent interest in modern regression modelling has focused on extending available ( mean) regression models by describing more general properties of the response distribution. An alternative approach is quantile regression where regression effects on the conditional quantile function of the response are assumed. While quantile regression can be seen as a generalization of median regression, expectiles as alternative are a generalized form of mean regression. Generally, quantiles provide a natural interpretation even beyond the 0.5 quantile, the median. A comparable simple interpretation is not available for expectiles beyond the 0.5 expectile, the mean. Nonetheless, expectiles have some interesting properties, some of which are discussed in this article. We contrast the two approaches and show how to get quantiles from a fine grid of expectiles. We compare such quantiles from expectiles with direct quantile estimates regarding efficiency. We also look at regression problems where both quantile and expectile curves have the undesirable property that neighbouring curves may cross each other. We propose a modified method to estimate non-crossing expectile curves based on splines. In an application, we look at the expected shortfall, a risk measure used in finance, which requires both expectiles and quantiles for estimation and which can be calculated easily with the proposed methods in the article."],["dc.description.sponsorship","German Research Foundation (DFG) [KA 1188/7-1]"],["dc.identifier.doi","10.1177/1471082X14561155"],["dc.identifier.gro","3149349"],["dc.identifier.isi","000363931700003"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/35773"],["dc.language.iso","en"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.relation.issn","1477-0342"],["dc.relation.issn","1471-082X"],["dc.title","Expectile and quantile regression-David and Goliath?"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","253"],["dc.bibliographiccitation.lastpage","290"],["dc.contributor.author","Fahrmeir, Ludwig"],["dc.contributor.author","Kneib, Thomas"],["dc.contributor.author","Lang, Stefan"],["dc.contributor.editor","Fahrmeir, Ludwig"],["dc.contributor.editor","Kneib, Thomas"],["dc.contributor.editor","Lang, Stefan"],["dc.date.accessioned","2017-09-07T11:47:51Z"],["dc.date.available","2017-09-07T11:47:51Z"],["dc.date.issued","2009"],["dc.identifier.doi","10.1007/978-3-642-01837-4_6"],["dc.identifier.gro","3149389"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/6060"],["dc.notes.intern","Kneib Crossref Import"],["dc.notes.status","public"],["dc.notes.submitter","chake"],["dc.publisher","Springer"],["dc.publisher.place","Berlin, Heidelberg"],["dc.relation.isbn","978-3-642-01836-7"],["dc.relation.ispartof","Regression: Modelle, Methoden und Anwendungen"],["dc.title","Gemischte Modelle"],["dc.type","book_chapter"],["dc.type.internalPublication","unknown"],["dc.type.peerReviewed","no"],["dspace.entity.type","Publication"]]

[["dc.contributor.author","Sobotka, F."],["dc.contributor.author","Schnabel, S."],["dc.contributor.author","Schulze Waltrup, L."],["dc.contributor.author","Kauermann, G."],["dc.contributor.author","Kneib, Thomas"],["dc.date.accessioned","2020-04-06T12:42:26Z"],["dc.date.available","2020-04-06T12:42:26Z"],["dc.date.issued","2012"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/63858"],["dc.title","expectreg: An R Package for Expectile Regression"],["dc.type","preprint"],["dc.type.internalPublication","unknown"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","731"],["dc.bibliographiccitation.journal","Statistica Sinica"],["dc.bibliographiccitation.lastpage","761"],["dc.bibliographiccitation.volume","14"],["dc.contributor.author","Fahrmeir, Ludwig"],["dc.contributor.author","Kneib, Thomas"],["dc.contributor.author","Lang, Stefan"],["dc.date.accessioned","2017-09-07T11:52:15Z"],["dc.date.available","2017-09-07T11:52:15Z"],["dc.date.issued","2004"],["dc.identifier.gro","3148154"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/5501"],["dc.notes.status","public"],["dc.notes.submitter","chake"],["dc.title","Penalized structured additive regression for space-time data: a Bayesian perspective"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dc.type.peerReviewed","no"],["dspace.entity.type","Publication"]]