Klein, Nadja

[["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"]]

[["dc.bibliographiccitation.firstpage","569"],["dc.bibliographiccitation.issue","4"],["dc.bibliographiccitation.journal","Journal of the Royal Statistical Society. Series C, Applied statistics"],["dc.bibliographiccitation.lastpage","591"],["dc.bibliographiccitation.volume","64"],["dc.contributor.author","Klein, Nadja"],["dc.contributor.author","Kneib, Thomas"],["dc.contributor.author","Klasen, Stephan"],["dc.contributor.author","Lang, Stefan"],["dc.date.accessioned","2017-09-07T11:47:18Z"],["dc.date.available","2017-09-07T11:47:18Z"],["dc.date.issued","2015"],["dc.description.abstract","We propose a unified Bayesian approach for multivariate structured additive distributional regression analysis comprising a huge class of continuous, discrete and latent multivariate response distributions, where each parameter of these potentially complex distributions is modelled by a structured additive predictor. The latter is an additive composition of different types of covariate effects, e.g. non‐linear effects of continuous covariates, random effects, spatial effects or interaction effects. Inference is realized by a generic, computationally efficient Markov chain Monte Carlo algorithm based on iteratively weighted least squares approximations and with multivariate Gaussian priors to enforce specific properties of functional effects. Applications to illustrate our approach include a joint model of risk factors for chronic and acute childhood undernutrition in India and ecological regressions studying the drivers of election results in Germany."],["dc.identifier.doi","10.1111/rssc.12090"],["dc.identifier.gro","3149328"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/5992"],["dc.language.iso","en"],["dc.notes.intern","Kneib Crossref Import"],["dc.notes.status","final"],["dc.notes.submitter","chake"],["dc.relation.issn","0035-9254"],["dc.title","Bayesian structured additive distributional regression for multivariate responses"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dc.type.peerReviewed","no"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","1135"],["dc.bibliographiccitation.issue","2"],["dc.bibliographiccitation.journal","The Annals of Applied Statistics"],["dc.bibliographiccitation.lastpage","1136"],["dc.bibliographiccitation.volume","10"],["dc.contributor.author","Klein, Nadja"],["dc.contributor.author","Kneib, Thomas"],["dc.contributor.author","Lang, Stefan"],["dc.contributor.author","Sohn, Alexander"],["dc.date.accessioned","2020-12-10T18:41:47Z"],["dc.date.available","2020-12-10T18:41:47Z"],["dc.date.issued","2016"],["dc.identifier.doi","10.1214/16-AOAS922"],["dc.identifier.fs","622641"],["dc.identifier.issn","1932-6157"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/77673"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.notes.status","final"],["dc.relation.iserratumof","/handle/2/6000"],["dc.relation.issn","1932-6157"],["dc.title","Correction: Bayesian structured additive distributional regression with an application to regional income inequality in Germany"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","unknown"],["dc.type.subtype","erratum_ja"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","225"],["dc.bibliographiccitation.journal","Insurance: Mathematics and Economics"],["dc.bibliographiccitation.lastpage","249"],["dc.bibliographiccitation.volume","55"],["dc.contributor.author","Klein, Nadja"],["dc.contributor.author","Denuit, Michel"],["dc.contributor.author","Lang, Stefan"],["dc.contributor.author","Kneib, Thomas"],["dc.date.accessioned","2017-09-07T11:47:16Z"],["dc.date.available","2017-09-07T11:47:16Z"],["dc.date.issued","2014"],["dc.description.abstract","Generalized additive models for location, scale and, shape define a flexible, semi-parametric class of regression models for analyzing insurance data in which the exponential family assumption for the response is relaxed. This approach allows the actuary to include risk factors not only in the mean but also in other key parameters governing the claiming behavior, like the degree of residual heterogeneity or the no-claim probability. In this broader setting, the Negative Binomial regression with cell-specific heterogeneity and the zero-inflated Poisson regression with cell-specific additional probability mass at zero are applied to model claim frequencies. New models for claim severities that can be applied either per claim or aggregated per year are also presented. Bayesian inference is based on efficient Markov chain Monte Carlo simulation techniques and allows for the simultaneous estimation of linear effects as well as of possible nonlinear effects, spatial variations and interactions between risk factors within the data set. To illustrate the relevance of this approach, a detailed case study is proposed based on the Belgian motor insurance portfolio studied in Denuit and Lang (2004)."],["dc.identifier.doi","10.1016/j.insmatheco.2014.02.001"],["dc.identifier.gro","3149311"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/5973"],["dc.language.iso","en"],["dc.notes.intern","Kneib Crossref Import"],["dc.notes.status","final"],["dc.notes.submitter","chake"],["dc.relation.issn","0167-6687"],["dc.title","Nonlife ratemaking and risk management with Bayesian generalized additive models for location, scale, and shape"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dc.type.peerReviewed","no"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","1024"],["dc.bibliographiccitation.issue","2"],["dc.bibliographiccitation.journal","The Annals of Applied Statistics"],["dc.bibliographiccitation.lastpage","1052"],["dc.bibliographiccitation.volume","9"],["dc.contributor.author","Klein, Nadja"],["dc.contributor.author","Kneib, Thomas"],["dc.contributor.author","Lang, Stefan"],["dc.contributor.author","Sohn, Alexander"],["dc.date.accessioned","2017-09-07T11:47:20Z"],["dc.date.available","2017-09-07T11:47:20Z"],["dc.date.issued","2015"],["dc.identifier.doi","10.1214/15-aoas823"],["dc.identifier.gro","3149335"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/6000"],["dc.notes.intern","Kneib Crossref Import"],["dc.notes.status","public"],["dc.notes.submitter","chake"],["dc.publisher","Institute of Mathematical Statistics"],["dc.relation.haserratum","/handle/2/77673"],["dc.relation.issn","1932-6157"],["dc.title","Bayesian structured additive distributional regression with an application to regional income inequality in Germany"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dc.type.peerReviewed","no"],["dspace.entity.type","Publication"]]