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","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.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"]]

[["dc.bibliographiccitation.firstpage","145"],["dc.bibliographiccitation.journal","Mathematical Biosciences"],["dc.bibliographiccitation.lastpage","154"],["dc.bibliographiccitation.volume","283"],["dc.contributor.author","Mamouridis, Valeria"],["dc.contributor.author","Klein, Nadja"],["dc.contributor.author","Kneib, Thomas"],["dc.contributor.author","Cadarso Suarez, Carmen"],["dc.contributor.author","Maynou, Francesc"],["dc.date.accessioned","2017-09-07T11:47:17Z"],["dc.date.available","2017-09-07T11:47:17Z"],["dc.date.issued","2017"],["dc.description.abstract","We analysed the landings per unit effort (LPUE) from the Barcelona trawl fleet targeting the red shrimp (Aristeus antennatus) using novel Bayesian structured additive distributional regression to gain a better understanding of the dynamics and determinants of variation in LPUE. The data set, covering a time span of 17 years, includes fleet-dependent variables (e.g. the number of trips performed by vessels), temporal variables (inter- and intra-annual variability) and environmental variables (the North Atlantic Oscillation index). Based on structured additive distributional regression, we evaluate (i) the gain in replacing purely linear predictors by additive predictors including nonlinear effects of continuous covariates, (ii) the inclusion of vessel-specific effects based on either fixed or random effects, (iii) different types of distributions for the response, and (iv) the potential gain in not only modelling the location but also the scale/shape parameter of these distributions. Our findings support that flexible model variants are indeed able to improve the fit considerably and that additional insights can be gained. Tools to select within several model specifications and assumptions are discussed in detail as well."],["dc.identifier.doi","10.1016/j.mbs.2016.11.016"],["dc.identifier.gro","3149320"],["dc.identifier.pmid","27914929"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/5983"],["dc.language.iso","en"],["dc.notes.intern","Kneib Crossref Import"],["dc.notes.status","final"],["dc.notes.submitter","chake"],["dc.relation.issn","0025-5564"],["dc.title","Structured additive distributional regression for analysing landings per unit effort in fisheries research"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","no"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","405"],["dc.bibliographiccitation.issue","509"],["dc.bibliographiccitation.journal","Journal of the American Statistical Association"],["dc.bibliographiccitation.lastpage","419"],["dc.bibliographiccitation.volume","110"],["dc.contributor.author","Klein, Nadja"],["dc.contributor.author","Kneib, Thomas"],["dc.contributor.author","Lang, Stefan"],["dc.date.accessioned","2017-09-07T11:47:47Z"],["dc.date.available","2017-09-07T11:47:47Z"],["dc.date.issued","2014"],["dc.description.abstract","Frequent problems in applied research preventing the application of the classical Poisson log-linear model for analyzing count data include overdispersion, an excess of zeros compared to the Poisson distribution, correlated responses, as well as complex predictor structures comprising nonlinear effects of continuous covariates, interactions or spatial effects. We propose a general class of Bayesian generalized additive models for zero-inflated and overdispersed count data within the framework of generalized additive models for location, scale, and shape where semiparametric predictors can be specified for several parameters of a count data distribution. As standard options for applied work we consider the zero-inflated Poisson, the negative binomial and the zero-inflated negative binomial distribution. The additive predictor specifications rely on basis function approximations for the different types of effects in combination with Gaussian smoothness priors. We develop Bayesian inference based on Markov chain Monte Carlo simulation techniques where suitable proposal densities are constructed based on iteratively weighted least squares approximations to the full conditionals. To ensure practicability of the inference, we consider theoretical properties like the involved question whether the joint posterior is proper. The proposed approach is evaluated in simulation studies and applied to count data arising from patent citations and claim frequencies in car insurances. For the comparison of models with respect to the distribution, we consider quantile residuals as an effective graphical device and scoring rules that allow us to quantify the predictive ability of the models. The deviance information criterion is used to select appropriate predictor specifications once a response distribution has been chosen. Supplementary materials for this article are available online."],["dc.identifier.doi","10.1080/01621459.2014.912955"],["dc.identifier.gro","3149359"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/6027"],["dc.language.iso","en"],["dc.notes.intern","Kneib Crossref Import"],["dc.notes.status","final"],["dc.notes.submitter","chake"],["dc.relation.issn","0162-1459"],["dc.title","Bayesian Generalized Additive Models for Location, Scale, and Shape for Zero-Inflated and Overdispersed Count Data"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dc.type.peerReviewed","no"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.issue","2"],["dc.bibliographiccitation.journal","Bayesian Analysis"],["dc.bibliographiccitation.volume","16"],["dc.contributor.author","Klein, Nadja"],["dc.contributor.author","Carlan, Manuel"],["dc.contributor.author","Kneib, Thomas"],["dc.contributor.author","Lang, Stefan"],["dc.contributor.author","Wagner, Helga"],["dc.date.accessioned","2021-06-01T09:42:17Z"],["dc.date.available","2021-06-01T09:42:17Z"],["dc.date.issued","2021"],["dc.identifier.doi","10.1214/20-BA1214"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/85204"],["dc.notes.intern","DOI-Import GROB-425"],["dc.relation.issn","1936-0975"],["dc.title","Bayesian Effect Selection in Structured Additive Distributional Regression Models"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]