Klein, Nadja

[["dc.bibliographiccitation.firstpage","413"],["dc.bibliographiccitation.issue","3"],["dc.bibliographiccitation.journal","Statistics in Medicine"],["dc.bibliographiccitation.lastpage","436"],["dc.bibliographiccitation.volume","38"],["dc.contributor.author","Klein, Nadja"],["dc.contributor.author","Kneib, Thomas"],["dc.contributor.author","Marra, Giampiero"],["dc.contributor.author","Radice, Rosalba"],["dc.contributor.author","Rokicki, Slawa"],["dc.contributor.author","McGovern, Mark E."],["dc.date.accessioned","2019-08-01T13:58:02Z"],["dc.date.available","2019-08-01T13:58:02Z"],["dc.date.issued","2019"],["dc.description.abstract","Bivariate copula regression allows for the flexible combination of two arbitrary, continuous marginal distributions with regression effects being placed on potentially all parameters of the resulting bivariate joint response distribution. Motivated by the risk factors for adverse birth outcomes, many of which are dichotomous, we consider mixed binary-continuous responses that extend the bivariate continuous framework to the situation where one response variable is discrete (more precisely, binary) whereas the other response remains continuous. Utilizing the latent continuous representation of binary regression models, we implement a penalized likelihood-based approach for the resulting class of copula regression models and employ it in the context of modeling gestational age and the presence/absence of low birth weight. The analysis demonstrates the advantage of the flexible specification of regression impacts including nonlinear effects of continuous covariates and spatial effects. Our results imply that racial and spatial inequalities in the risk factors for infant mortality are even greater than previously suggested."],["dc.identifier.doi","10.1002/sim.7985"],["dc.identifier.pmid","30334275"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/62257"],["dc.language.iso","en"],["dc.notes.status","final"],["dc.relation.eissn","1097-0258"],["dc.relation.issn","0277-6715"],["dc.title","Mixed binary-continuous copula regression models with application to adverse birth outcomes"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.contributor.author","Mamouridis, Valeria"],["dc.contributor.author","Klein, Nadja"],["dc.contributor.author","Kneib, Thomas"],["dc.contributor.author","Cadarso, Carmen"],["dc.contributor.author","Maynou, Francesc"],["dc.date.accessioned","2020-04-03T13:26:48Z"],["dc.date.available","2020-04-03T13:26:48Z"],["dc.date.issued","2013-10"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/63627"],["dc.title","Extended Additive Regression for Analysing LPUE Indices in Fishery Research"],["dc.type","preprint"],["dc.type.internalPublication","unknown"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","663"],["dc.bibliographiccitation.issue","4"],["dc.bibliographiccitation.journal","European Review of Agricultural Economics"],["dc.bibliographiccitation.lastpage","698"],["dc.bibliographiccitation.volume","43"],["dc.contributor.author","März, Alexander"],["dc.contributor.author","Klein, Nadja"],["dc.contributor.author","Kneib, Thomas"],["dc.contributor.author","Mußhoff, Oliver"],["dc.date.accessioned","2017-09-07T11:47:18Z"],["dc.date.available","2017-09-07T11:47:18Z"],["dc.date.issued","2016"],["dc.description.abstract","Empirical studies on farmland rental rates so far have predominantly concentrated on modelling conditional means using spatial autoregressive models. While these models only focus on the central tendency of the response variable, quantile regression provides more detailed insight by modelling different points of the conditional distribution as a function of covariates. Based on data from the German agricultural census, this article contributes to the agricultural economics literature by modelling conditional quantiles of farmland rental rates semi-parametrically using Bayesian geoadditive quantile regression. Our results stress the importance of using semi-parametric regression models, as several covariates influence rental rates in an explicit non-linear way. Moreover, our analysis allows us to uncover potential heterogeneities of the estimated effects across the conditional distribution of rental rates. By explicitly modelling and visually presenting the spatial effects, we also provide additional insight into the spatial structure of German farmland rental rates."],["dc.identifier.doi","10.1093/erae/jbv028"],["dc.identifier.gro","3149313"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/5975"],["dc.language.iso","en"],["dc.notes.intern","Kneib Crossref Import"],["dc.notes.status","final"],["dc.notes.submitter","chake"],["dc.relation.issn","0165-1587"],["dc.title","Analysing farmland rental rates using Bayesian geoadditive quantile regression"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","no"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","1104-1121"],["dc.bibliographiccitation.issue","6"],["dc.bibliographiccitation.journal","Biometrical journal. Biometrische Zeitschrift"],["dc.bibliographiccitation.lastpage","1121"],["dc.bibliographiccitation.volume","59"],["dc.contributor.author","Waldmann, Elisabeth"],["dc.contributor.author","Taylor-Robinson, David"],["dc.contributor.author","Klein, Nadja"],["dc.contributor.author","Kneib, Thomas"],["dc.contributor.author","Pressler, Tania"],["dc.contributor.author","Schmid, Matthias"],["dc.contributor.author","Mayr, Andreas"],["dc.date.accessioned","2018-03-13T15:00:13Z"],["dc.date.available","2018-03-13T15:00:13Z"],["dc.date.issued","2017"],["dc.description.abstract","Joint models for longitudinal and time-to-event data have gained a lot of attention in the last few years as they are a helpful technique clinical studies where longitudinal outcomes are recorded alongside event times. Those two processes are often linked and the two outcomes should thus be modeled jointly in order to prevent the potential bias introduced by independent modeling. Commonly, joint models are estimated in likelihood-based expectation maximization or Bayesian approaches using frameworks where variable selection is problematic and that do not immediately work for high-dimensional data. In this paper, we propose a boosting algorithm tackling these challenges by being able to simultaneously estimate predictors for joint models and automatically select the most influential variables even in high-dimensional data situations. We analyze the performance of the new algorithm in a simulation study and apply it to the Danish cystic fibrosis registry that collects longitudinal lung function data on patients with cystic fibrosis together with data regarding the onset of pulmonary infections. This is the first approach to combine state-of-the art algorithms from the field of machine-learning with the model class of joint models, providing a fully data-driven mechanism to select variables and predictor effects in a unified framework of boosting joint models."],["dc.identifier.doi","10.1002/bimj.201600158"],["dc.identifier.pmid","28321912"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/13023"],["dc.language.iso","en"],["dc.notes.status","zu prüfen"],["dc.relation.eissn","1521-4036"],["dc.title","Boosting joint models for longitudinal and time-to-event data"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","53"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Clinical Genetics"],["dc.bibliographiccitation.lastpage","65"],["dc.bibliographiccitation.volume","83"],["dc.contributor.author","Shoukier, M."],["dc.contributor.author","Klein, Nadja"],["dc.contributor.author","Auber, B."],["dc.contributor.author","Wickert, J."],["dc.contributor.author","Schroeder, J."],["dc.contributor.author","Zoll, Barbara"],["dc.contributor.author","Burfeind, P."],["dc.contributor.author","Bartels, I."],["dc.contributor.author","Alsat, E. A."],["dc.contributor.author","Lingen, M."],["dc.contributor.author","Grzmil, P."],["dc.contributor.author","Schulze, S."],["dc.contributor.author","Keyser, J."],["dc.contributor.author","Weise, Dagmar"],["dc.contributor.author","Borchers, M."],["dc.contributor.author","Hobbiebrunken, E."],["dc.contributor.author","Roebl, M."],["dc.contributor.author","Gärtner, Jutta"],["dc.contributor.author","Brockmann, Knut"],["dc.contributor.author","Zirn, Birgit"],["dc.date.accessioned","2017-09-07T11:48:19Z"],["dc.date.available","2017-09-07T11:48:19Z"],["dc.date.issued","2013"],["dc.description.abstract","Array comparative genomic hybridization (array CGH) is now widely adopted as a first-tier clinical diagnostic test in individuals with unexplained developmental delay/intellectual disability (DD/ID) and congenital anomalies. Our study aimed at enlarging the phenotypic spectrum associated with clinically relevant copy number variants (CNVs) as well as delineating clinical criteria, which may help separating patients with pathogenic CNVs from those without pathogenic CNVs. We performed a retrospective review of clinical and array CGH data of 342 children with unexplained DD/ID. The phenotypic features of patients with clinically significant CNV were compared with those without pathogenic CNVs. Array CGH detected pathogenic CNVs in 13.2% of the patients. Congenital anomalies, especially heart defects, as well as primary microcephaly, short stature and failure to thrive were clearly more frequent in children with pathogenic CNVs compared with children with normal array CGH results. Thus, we assume that in patients with unexplained DD/ID, array CGH will more probably detect a significant CNV if any of these features is part of the patient's phenotype."],["dc.identifier.doi","10.1111/j.1399-0004.2012.01850.x"],["dc.identifier.gro","3142418"],["dc.identifier.isi","000312544000011"],["dc.identifier.pmid","22283495"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/8063"],["dc.notes.intern","WoS Import 2017-03-10"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","PUB_WoS_Import"],["dc.relation.issn","0009-9163"],["dc.title","Array CGH in patients with developmental delay or intellectual disability: are there phenotypic clues to pathogenic copy number variants?"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.subtype","original"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","841"],["dc.bibliographiccitation.issue","4"],["dc.bibliographiccitation.journal","Statistics and Computing"],["dc.bibliographiccitation.lastpage","860"],["dc.bibliographiccitation.volume","26"],["dc.contributor.author","Klein, Nadja"],["dc.contributor.author","Kneib, Thomas"],["dc.date.accessioned","2017-09-07T11:47:48Z"],["dc.date.available","2017-09-07T11:47:48Z"],["dc.date.issued","2015"],["dc.identifier.doi","10.1007/s11222-015-9573-6"],["dc.identifier.gro","3149374"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/6044"],["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","Simultaneous inference in structured additive conditional copula regression models: a unifying Bayesian approach"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dc.type.peerReviewed","no"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.journal","Scandinavian Journal of Statistics"],["dc.contributor.author","Klein, Nadja"],["dc.contributor.author","Hothorn, Torsten"],["dc.contributor.author","Barbanti, Luisa"],["dc.contributor.author","Kneib, Thomas"],["dc.date.accessioned","2021-04-14T08:23:19Z"],["dc.date.available","2021-04-14T08:23:19Z"],["dc.date.issued","2020"],["dc.identifier.doi","10.1111/sjos.12501"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/80873"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-399"],["dc.relation.eissn","1467-9469"],["dc.relation.issn","0303-6898"],["dc.title","Multivariate conditional transformation models"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","197"],["dc.bibliographiccitation.issue","3"],["dc.bibliographiccitation.journal","Entomologia Experimentalis et Applicata"],["dc.bibliographiccitation.lastpage","206"],["dc.bibliographiccitation.volume","139"],["dc.contributor.author","Kabouw, P."],["dc.contributor.author","Kos, Mark Z."],["dc.contributor.author","Kleine, S."],["dc.contributor.author","Vockenhuber, E. A."],["dc.contributor.author","Van Loon, J. J. W. A."],["dc.contributor.author","van der Putten, Wim H."],["dc.contributor.author","van Dam, N. M."],["dc.contributor.author","Biere, A."],["dc.date.accessioned","2018-11-07T08:55:41Z"],["dc.date.available","2018-11-07T08:55:41Z"],["dc.date.issued","2011"],["dc.description.abstract","Belowground communities can affect interactions between plants and aboveground insect communities. Such belowground-aboveground interactions are known to depend on the composition of belowground communities, as well as on the plant species that mediates these interactions. However, it is largely unknown whether the effect of belowground communities on aboveground plant-insect interactions also depends on genotypic variation within the plant species that mediates the interaction. To assess whether the outcome of belowground-aboveground interactions can be affected by plant genotype, we selected two white cabbage cultivars [Brassica oleracea L. var. capitata (Brassicaceae)]. From previous studies, it is known that these cultivars differ in their chemistry and belowground and aboveground multitrophic interactions. Belowground, we inoculated soils of the cultivars with either nematodes or microorganisms and included a sterilized soil as a control treatment. Aboveground, we quantified aphid [Brevicoryne brassicae (L.) (Hemiptera: Aphididae)] population development and parasitoid [Diaeretiella rapae (McIntosh) (Hymenoptera: Braconidae)] fitness parameters. The cultivar that sustained highest aphid numbers also had the best parasitoid performance. Soil treatment affected aphid population sizes: microorganisms increased aphid population growth. Soil treatments did not affect parasitoid performance. Cultivars differed in their amino acid concentration, leaf relative growth rate, and root, shoot, and phloem glucosinolate composition but showed similar responses of these traits to soil treatments. Consistent with this observation, no interactions were found between cultivar and soil treatment for aphid population growth or parasitoid performance. Overall, the aboveground community was more affected by cultivar, which was associated with glucosinolate profiles, than by soil community."],["dc.description.sponsorship","ERGO, Netherlands Organization for Scientific Research [83806012]"],["dc.identifier.doi","10.1111/j.1570-7458.2011.01123.x"],["dc.identifier.isi","000290686800002"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/22963"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Wiley-blackwell"],["dc.relation.issn","0013-8703"],["dc.title","Effects of soil organisms on aboveground multitrophic interactions are consistent between plant genotypes mediating the interaction"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","PII S0165-1781(02)00249-4"],["dc.bibliographiccitation.firstpage","17"],["dc.bibliographiccitation.issue","1-2"],["dc.bibliographiccitation.journal","Psychiatry Research"],["dc.bibliographiccitation.lastpage","27"],["dc.bibliographiccitation.volume","113"],["dc.contributor.author","Riemann, D."],["dc.contributor.author","Klein, T."],["dc.contributor.author","Rodenbeck, Andrea"],["dc.contributor.author","Feige, B."],["dc.contributor.author","Horny, A."],["dc.contributor.author","Hummel, R."],["dc.contributor.author","Weske, G."],["dc.contributor.author","Al-Shajlawi, A."],["dc.contributor.author","Voderholzer, U."],["dc.date.accessioned","2018-11-07T09:42:15Z"],["dc.date.available","2018-11-07T09:42:15Z"],["dc.date.issued","2002"],["dc.description.abstract","The present study investigated evening and nocturnal serum cortisol and melatonin concentrations in patients with primary insomnia to test if this clinical condition is accompanied by an increase of cortisol secretion and a simultaneous decrease of nocturnal melatonin production. Ten drug-free patients (4 males, 6 females) with primary insomnia (mean age +/- S.D.: 39.2 +/- 9.1 years) and 10 age- and gender-matched healthy controls participated in the study. All subjects spent three consecutive nights in the sleep laboratory with polysomnography. Measurement of cortisol and melatonin (from 19:00 h to 09:00 h) was performed prior to and during the last laboratory night. Contrary to expectation. cortisol secretion did not differ between healthy controls and insomniac patients. On the other hand, nocturnal melatonin production was significantly diminished in insomniac patients. Polysomnographically determined sleep patterns. in contrast to subjective ratings of sleep, demonstrated only minor alterations of sleep in the insomniac group. The lack of increased cortisol secretion in the patients with primary insomnia indicates that results from studies on the biological consequences of experimental sleep loss in healthy subjects cannot be applied to primary insomnia in general, especially if there are only minor objective sleep alterations. In spite of the negligible objective sleep disturbances in the present sample, nocturnal melatonin production was reduced, which tentatively suggests a role for this hormone in primary insomniacs. The pathophysiological significance of this finding is, however, still a matter of debate. (C) 2002 Elsevier Science Ireland Ltd. All rights reserved."],["dc.identifier.doi","10.1016/S0165-1781(02)00249-4"],["dc.identifier.isi","000179989900002"],["dc.identifier.pmid","12467942"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/33914"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Elsevier Sci Ireland Ltd"],["dc.relation.issn","0165-1781"],["dc.title","Nocturnal cortisol and melatonin secretion in primary insomnia"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","S0167947321002164"],["dc.bibliographiccitation.firstpage","107382"],["dc.bibliographiccitation.journal","Computational Statistics & Data Analysis"],["dc.bibliographiccitation.volume","168"],["dc.contributor.author","Wiemann, Paul F.V."],["dc.contributor.author","Klein, Nadja"],["dc.contributor.author","Kneib, Thomas"],["dc.date.accessioned","2022-04-01T10:02:23Z"],["dc.date.available","2022-04-01T10:02:23Z"],["dc.date.issued","2022"],["dc.identifier.doi","10.1016/j.csda.2021.107382"],["dc.identifier.pii","S0167947321002164"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/105895"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-530"],["dc.relation.issn","0167-9473"],["dc.rights.uri","https://www.elsevier.com/tdm/userlicense/1.0/"],["dc.title","Correcting for sample selection bias in Bayesian distributional regression models"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]