Bergherr, Elisabeth

[["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","326"],["dc.bibliographiccitation.issue","4"],["dc.bibliographiccitation.journal","Statistical Modelling"],["dc.bibliographiccitation.lastpage","344"],["dc.bibliographiccitation.volume","15"],["dc.contributor.author","Waldmann, Elisabeth"],["dc.contributor.author","Kneib, Thomas"],["dc.date.accessioned","2022-06-08T07:57:14Z"],["dc.date.available","2022-06-08T07:57:14Z"],["dc.date.issued","2014"],["dc.description.abstract","Quantile regression (QR) has become a widely used tool to study the impact of covariates on quantiles of a response distribution. QR provides a detailed description of the conditional response when considering a dense set of quantiles, without assuming a closed form for its distribution. The Bayesian version of QR, which can be implemented by considering the asymmetric Laplace distribution (ALD) as an auxiliary error distribution, is an attractive alternative to other methods because it returns knowledge on the whole parameter distribution instead of solely point estimations. While for the univariate case there has been a lot of development in the last few years, multivariate responses have only been treated to a little extent in the literature, especially in the Bayesian case. By using a multivariate version of the location scale mixture representation for the ALD, we are able to apply inference techniques developed for multivariate Gaussian models on multivariate quantile regression and make thus the impact of covariates on the quantiles of more than one dependent variable feasible. The model structure also facilitates the determination of conditional correlations between bivariate responses on different quantile levels after adjusting for covariate effects."],["dc.identifier.doi","10.1177/1471082x14551247"],["dc.identifier.gro","3149375"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/110033"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/6045"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-575"],["dc.notes.status","public"],["dc.notes.submitter","chake"],["dc.publisher","SAGE Publications"],["dc.relation.eissn","1477-0342"],["dc.relation.issn","1471-082X"],["dc.rights.uri","http://journals.sagepub.com/page/policies/text-and-data-mining-license"],["dc.title","Bayesian bivariate quantile regression"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dc.type.peerReviewed","no"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","1539"],["dc.bibliographiccitation.issue","6"],["dc.bibliographiccitation.journal","Statistics and Computing"],["dc.bibliographiccitation.lastpage","1553"],["dc.bibliographiccitation.volume","27"],["dc.contributor.author","Waldmann, Elisabeth"],["dc.contributor.author","Sobotka, Fabian"],["dc.contributor.author","Kneib, Thomas"],["dc.date.accessioned","2018-03-13T15:04:18Z"],["dc.date.available","2018-03-13T15:04:18Z"],["dc.date.issued","2016"],["dc.identifier.doi","10.1007/s11222-016-9703-9"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/13024"],["dc.notes.status","zu prüfen"],["dc.title","Bayesian regularisation in geoadditive expectile regression"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","223"],["dc.bibliographiccitation.issue","3"],["dc.bibliographiccitation.journal","Statistical Modelling"],["dc.bibliographiccitation.lastpage","252"],["dc.bibliographiccitation.volume","13"],["dc.contributor.author","Waldmann, Elisabeth"],["dc.contributor.author","Kneib, Thomas"],["dc.contributor.author","Yue, Yu Ryan"],["dc.contributor.author","Lang, Stefan"],["dc.contributor.author","Flexeder, Claudia"],["dc.date.accessioned","2017-09-07T11:47:44Z"],["dc.date.available","2017-09-07T11:47:44Z"],["dc.date.issued","2013"],["dc.description.abstract","Quantile regression provides a convenient framework for analyzing the impact of covariates on the complete conditional distribution of a response variable instead of only the mean. While frequentist treatments of quantile regression are typically completely nonparametric, a Bayesian formulation relies on assuming the asymmetric Laplace distribution as auxiliary error distribution that yields posterior modes equivalent to frequentist estimates. In this paper, we utilize a location-scale mixture of normals representation of the asymmetric Laplace distribution to transfer different flexible modelling concepts from Gaussian mean regression to Bayesian semiparametric quantile regression. In particular, we will consider high-dimensional geoadditive models comprising LASSO regularization priors and mixed models with potentially non-normal random effects distribution modeled via a Dirichlet process mixture. These extensions are illustrated using two large-scale applications on net rents in Munich and longitudinal measurements on obesity among children. The impact of the likelihood misspecification that underlies the Bayesian formulation of quantile regression is studied in terms of simulations."],["dc.identifier.doi","10.1177/1471082x13480650"],["dc.identifier.fs","598863"],["dc.identifier.gro","3149355"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/10830"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/6023"],["dc.language.iso","en"],["dc.notes","This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively."],["dc.notes","Financial support from the German Research Foundation (DFG), grant KN 922/4-1\r\nis gratefully acknowledged."],["dc.notes.intern","Kneib Crossref Import"],["dc.notes.status","final"],["dc.notes.submitter","chake"],["dc.publisher","SAGE Publications"],["dc.publisher.place","New Delhi"],["dc.relation.eissn","1477-0342"],["dc.relation.issn","1471-082X"],["dc.relation.orgunit","Wirtschaftswissenschaftliche Fakultät"],["dc.rights","Goescholar"],["dc.rights.access","openAccess"],["dc.rights.uri","https://goescholar.uni-goettingen.de/licenses"],["dc.subject","Quantile Regression; Geodditive Regression; MCMC; LASSO Regularization; Dirichlet Process"],["dc.title","Bayesian semiparametric additive quantile regression"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","no"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

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