Prof. Dr. Thomas Kneib

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  • 2023 | Preprint
    ​ ​Rappl A, Kneib T, Lang S, Bergherr E. ​"Spatial Joint Models through Bayesian Structured Piece-wise Additive Joint Modelling for Longitudinal and Time-to-Event Data"​. ​​2023. Available from: http://arxiv.org/abs/2302.07020v1​
    ​Details 
  • 2022 | Journal Article | Research Paper | 
    ​ ​Martins R, Sousa B, Kneib T, Hohberg M, Klein N, Duarte E, et al. ​Is age at menopause decreasing? – The consequences of not completing the generational cohort​. ​​BMC Medical Research Methodology. ​2022;​22​(1). ​doi:10.1186/s12874-022-01658-x. 
    ​Details  DOI 
  • 2022 | Journal Article
    ​ ​Wiemann PF, Klein N, Kneib T. ​Correcting for sample selection bias in Bayesian distributional regression models​. ​​Computational Statistics & Data Analysis. ​2022;​168​:​​107382​. ​doi:10.1016/j.csda.2021.107382. 
    ​Details  DOI 
  • 2022 | Journal Article | 
    ​ ​Seufert JD, Python A, Weisser C, Cisneros E, Kis-Katos K, Kneib T. ​Mapping ex ante risks of COVID‐19 in Indonesia using a Bayesian geostatistical model on airport network data​. ​​Journal of the Royal Statistical Society: Series A (Statistics in Society). ​2022;. ​doi:10.1111/rssa.12866. 
    ​Details  DOI 
  • 2022 | Journal Article | 
    ​ ​Marmolejo-Ramos F, Barrera-Causil C, Kuang S, Fazlali Z, Wegener D, Kneib T, et al. ​Generalised exponential-Gaussian distribution: a method for neural reaction time analysis​. ​​Cognitive Neurodynamics. ​2022;. ​doi:10.1007/s11571-022-09813-2. 
    ​Details  DOI 
  • 2022 | Journal Article | Research Paper | 
    ​ ​Marques I, Kneib T, Klein N. ​Mitigating spatial confounding by explicitly correlating Gaussian random fields​. ​​Environmetrics. ​2022;​33​(5). ​doi:10.1002/env.2727. 
    ​Details  DOI 
  • 2022 | Journal Article | 
    ​ ​Weisser C, Gerloff C, Thielmann A, Python A, Reuter A, Kneib T, et al. ​Pseudo-document simulation for comparing LDA, GSDMM and GPM topic models on short and sparse text using Twitter data​. ​​Computational Statistics. ​2022;. ​doi:10.1007/s00180-022-01246-z. 
    ​Details  DOI 
  • 2022 | Journal Article | 
    ​ ​Marques I, Kneib T, Klein N. ​A non-stationary model for spatially dependent circular response data based on wrapped Gaussian processes​. ​​Statistics and Computing. ​2022;​32​(5). ​doi:10.1007/s11222-022-10136-9. 
    ​Details  DOI 
  • 2022 | Journal Article | 
    ​ ​Carlan M, Kneib T. ​Bayesian discrete conditional transformation models​. ​​Statistical Modelling. ​2022;. ​doi:10.1177/1471082X221114177. 
    ​Details  DOI 
  • 2022 | Journal Article | 
    ​ ​Marmolejo‐Ramos F, Tejo M, Brabec M, Kuzilek J, Joksimovic S, Kovanovic V, et al. ​Distributional regression modeling via generalized additive models for location, scale, and shape: An overview through a data set from learning analytics​. ​​Wiley Interdisciplinary Reviews. Data Mining and Knowledge Discovery. ​2022;. ​doi:10.1002/widm.1479. 
    ​Details  DOI 
  • 2022 | Journal Article
    ​ ​Melis GG, Kneib T. ​In memory of Carmen María Cadarso Suárez (1960–2022)​. ​​Biometrical Journal. ​2022;​64​(7):​​1159​-1160​. ​doi:10.1002/bimj.202270075. 
    ​Details  DOI 
  • 2021 | Journal Article | Research Paper | 
    ​ ​Lasser J, Manik D, Silbersdorff A, Säfken B, Kneib T. ​Introductory data science across disciplines, using Python, case studies, and industry consulting projects​. ​​Teaching Statistics. ​2021;​43​:​​S190​-S200​. ​doi:10.1111/test.12243. 
    ​Details  DOI 
  • 2021 | Journal Article
    ​ ​Hambuckers J, Kneib T. ​Smooth-Transition Regression Models for Non-Stationary Extremes​. ​​Journal of Financial Econometrics. ​2021;. ​doi:10.1093/jjfinec/nbab005. 
    ​Details  DOI 
  • 2021 | Journal Article
    ​ ​Säfken B, Rügamer D, Kneib T, Greven S. ​Conditional Model Selection in Mixed-Effects Models with cAIC4​. ​​Journal of Statistical Software. ​2021;​99​(8). ​doi:10.18637/jss.v099.i08. 
    ​Details  DOI 
  • 2021 | Journal Article
    ​ ​Spiegel E, Kneib T, von Gablenz P, Otto‐Sobotka F. ​Generalized expectile regression with flexible response function​. ​​Biometrical Journal. ​2021;. ​doi:10.1002/bimj.202000203. 
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  • 2021 | Journal Article
    ​ ​Seiler J, Harttgen K, Kneib T, Lang S. ​Modelling children's anthropometric status using Bayesian distributional regression merging socio-economic and remote sensed data from South Asia and sub-Saharan Africa​. ​​Economics and Human Biology. ​2021;​40​:​​100950​. ​doi:10.1016/j.ehb.2020.100950. 
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  • 2021 | Journal Article
    ​ ​Klein N, Carlan M, Kneib T, Lang S, Wagner H. ​Bayesian Effect Selection in Structured Additive Distributional Regression Models​. ​​Bayesian Analysis. ​2021;​16​(2). ​doi:10.1214/20-BA1214. 
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  • 2021 | Journal Article | Research Paper
    ​ ​Seidel D, Annighöfer P, Thielman A, Seifert QE, Thauer, Jan-Henrik, Glatthorn J, et al. ​Predicting Tree Species From 3D Laser Scanning Point Clouds Using Deep Learning​. ​​Frontiers in Plant Science. ​2021;​12​. ​doi:10.3389/fpls.2021.635440. 
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  • 2021 | Journal Article | Research Paper | 
    ​ ​Udy K, Fritsch M, Meyer KM, Grass I, Hanß S, Hartig F, et al. ​Environmental heterogeneity predicts global species richness patterns better than area​. ​​Global Ecology and Biogeography. ​2021;​30​(4):​​842​-851​. ​doi:10.1111/geb.13261. 
    ​Details  DOI 
  • 2021 | Journal Article | 
    ​ ​Stadlmann S, Kneib T. ​Interactively visualizing distributional regression models with distreg.vis​. ​​Statistical Modelling. ​2021;​22​(6):​​527​-545​. ​doi:10.1177/1471082X211007308. 
    ​Details  DOI 
  • 2021 | Journal Article | Research Paper | 
    ​ ​Hohberg M, Donat F, Marra G, Kneib T. ​Beyond unidimensional poverty analysis using distributional copula models for mixed ordered‐continuous outcomes​. ​​Journal of the Royal Statistical Society: Series C (Applied Statistics). ​2021;​70​(5):​​1365​-1390​. ​doi:10.1111/rssc.12517. 
    ​Details  DOI 
  • 2020 | Journal Article | 
    ​ ​Kneib T, Otto-Sobotka F, Spiegel E. ​Spatio-temporal expectile regression models​. ​​Statistical Modelling. ​2020;​20​(4):​​386​-409​. ​doi:10.1177/1471082X19829945. 
    ​Details  DOI 
  • 2020 | Journal Article
    ​ ​Michaelis P, Klein N, Kneib T. ​Mixed Discrete‐Continuous Regression – A Novel Approach Based on Weight Functions​. ​​Stat. ​2020;. ​doi:10.1002/sta4.277. 
    ​Details  DOI 
  • 2020 | Preprint
    ​ ​Seebaß JV, Schlüter J, Wacker B, Kneib T. ​Application of an additive structured copula regression on the joint wind speed and wind direction distribution​. ​​2020. 
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  • 2020 | Preprint
    ​ ​Wacker B, Kneib T, Schlüter J. ​On Existence and Uniqueness of Maximum Log-Likelihood Parameter Estimation for Two-Parameter Weibull Distributions​. ​​2020. 
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  • 2020 | Report
    ​ ​Hambuckers J, Kneib T. ​Smooth Transition Regression Models for Non-Stationary Extremes​. ​​2020. doi: https://doi.org/10.2139/ssrn.3541718 
    ​Details  DOI 
  • 2020 | Journal Article
    ​ ​Klein N, Herwartz H, Kneib T. ​Modelling regional patterns of inefficiency: A Bayesian approach to geoadditive panel stochastic frontier analysis with an application to cereal production in England and Wales​. ​​Journal of Econometrics. ​2020;​214​(2):​​513​-539​. ​doi:10.1016/j.jeconom.2019.07.003. 
    ​Details  DOI 
  • 2020 | Journal Article
    ​ ​Klein N, Hothorn T, Barbanti L, Kneib T. ​Multivariate conditional transformation models​. ​​Scandinavian Journal of Statistics. ​2020;. ​doi:10.1111/sjos.12501. 
    ​Details  DOI 
  • 2020 | Book Chapter
    ​ ​Klein N, Kneib T, Marra G, Radice R. ​Bayesian mixed binary-continuous copula regression with an application to childhood undernutrition​. ​In: Dortet-Bernadet, Jean-Luc, Fan Y, Nott D, Smith MS​, editors. ​Flexible Bayesian Regression Modelling. ​Elsevier; ​2020. p. 121​-152​. ​doi:10.1016/B978-0-12-815862-3.00011-1. 
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  • 2020 | Journal Article
    ​ ​Santos B, Kneib T. ​Noncrossing structured additive multiple-output Bayesian quantile regression models​. ​​Statistics and Computing. ​2020;. ​doi:10.1007/s11222-020-09925-x. 
    ​Details  DOI 
  • 2020 | Conference Paper
    ​ ​Roeder J, Muntermann J, Kneib T. ​Towards a Taxonomy for Data Heterogeneity​. ​​Proceedings of Internationale Tagung Wirtschaftsinformatik 2020. Internationale Tagung Wirtschaftsinformatik 2020; ​2020-03-09​ - 2020-03-11​; ​​Potsdam. ​2020. 
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  • 2020 | Journal Article
    ​ ​Voncken L, Kneib T, Albers CJ, Umlauf N, Timmerman ME. ​Bayesian Gaussian distributional regression models for more efficient norm estimation​. ​​British Journal of Mathematical and Statistical Psychology. ​2020;​74​(1):​​99​-117​. ​doi:10.1111/bmsp.12206. 
    ​Details  DOI 
  • 2020 | Journal Article | Research Paper
    ​ ​Marques I, Klein N, Kneib T. ​Non-stationary spatial regression for modelling monthly precipitation in Germany​. ​​Spatial Statistics. ​2020;​40​:​​100386​. ​doi:10.1016/j.spasta.2019.100386. 
    ​Details  DOI 
  • 2020 | Journal Article
    ​ ​Briseño Sanchez G, Hohberg M, Groll A, Kneib T. ​Flexible instrumental variable distributional regression​. ​​Journal of the Royal Statistical Society: Series A (Statistics in Society). ​2020;​183​(4):​​1553​-1574​. ​doi:10.1111/rssa.12598. 
    ​Details  DOI 
  • 2020 | Journal Article | 
    ​ ​Kneib T. ​Comments on: Inference and computation with Generalized Additive Models and their extensions​. ​​TEST. ​2020;​29​(2):​​351​-353​. ​doi:10.1007/s11749-020-00713-3. 
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  • 2020 | Journal Article | 
    ​ ​van der Wurp H, Groll A, Kneib T, Marra G, Radice R. ​Generalised joint regression for count data: a penalty extension for competitive settings​. ​​Statistics and Computing. ​2020;​30​(5):​​1419​-1432​. ​doi:10.1007/s11222-020-09953-7. 
    ​Details  DOI 
  • 2020 | Journal Article | Editorial Contribution (Editorial, Introduction, Epilogue) | 
    ​ ​Kauermann G, Kneib T, Okhrin Y. ​Editorial​. ​​Advances in Statistical Analysis. ​2020;​104​(1):​​1​-3​. ​doi:10.1007/s10182-020-00361-w. 
    ​Details  DOI 
  • 2020 | Journal Article | 
    ​ ​Hohberg M, Pütz P, Kneib T. ​Treatment effects beyond the mean using distributional regression: Methods and guidance​. ​​PLoS One. ​2020;​15​(2). ​doi:10.1371/journal.pone.0226514. 
    ​Details  DOI  PMID  PMC 
  • 2019 | Preprint
    ​ ​Santos B, Kneib T. ​Noncrossing structured additive multiple-output Bayesian quantile regression models​. ​​2019. 
    ​Details 
  • 2019 | Preprint
    ​ ​van der Wurp H, Groll AH, Kneib T, Marra G. ​Generalised Joint Regression for Count Data with a Focus on Modelling Football Matches​. ​​2019. 
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  • 2019 | Preprint
    ​ ​Hambuckers J, Kneib T. ​Operational risk, uncertainty, and the economy: a smooth transition extreme value approach​. ​​2019. 
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  • 2019 | Journal Article
    ​ ​Klein N, Entwistle A, Rosenberger A, Kneib T, Bickeböller H. ​Candidate-gene association analysis for a continuous phenotype with a spike at zero using parent-offspring trios​. ​​Journal of Applied Statistics. ​2019;:​​1​-15​. ​doi:10.1080/02664763.2019.1704226. 
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  • 2019 | Book Chapter
    ​ ​Hohberg M, Silbersdorff A, Kneib T. ​Mehr als Durchschnittsstatistik: ​Eine kritische Einführung in Regressionsmethoden jenseits des Mittelwertes​. ​​Perspektiven einer pluralen Ökonomik. ​2019. p. 231​-255​. ​doi:10.1007/978-3-658-16145-3_10. 
    ​Details  DOI 
  • 2019 | Journal Article
    ​ ​Groll A, Hambuckers J, Kneib T, Umlauf N. ​LASSO-type penalization in the framework of generalized additive models for location, scale and shape​. ​​Computational Statistics & Data Analysis. ​2019;​140​:​​59​-73​. ​doi:10.1016/j.csda.2019.06.005. 
    ​Details  DOI 
  • 2019 | Journal Article
    ​ ​Thaden H, Klein N, Kneib T. ​Multivariate effect priors in bivariate semiparametric recursive Gaussian models​. ​​Computational Statistics & Data Analysis. ​2019;​137​:​​51​-66​. ​doi:10.1016/j.csda.2018.12.004. 
    ​Details  DOI 
  • 2019 | Journal Article
    ​ ​Kneib T, Klein N, Lang S, Umlauf N. ​Modular regression - a Lego system for building structured additive distributional regression models with tensor product interactions​. ​​Test: an official journal of the Spanish Society of Statistics and Operations Research. ​2019;​28​(1):​​1​-39​. ​doi:10.1007/s11749-019-00631-z. 
    ​Details  DOI 
  • 2019 | Journal Article
    ​ ​Kneib T, Klein N, Lang S, Umlauf N. ​Rejoinder on: Modular regression - a Lego system for building structured additive distributional regression models with tensor product interactions​. ​​Test: an official journal of the Spanish Society of Statistics and Operations Research. ​2019;​28​(1):​​55​-59​. ​doi:10.1007/s11749-019-00636-8. 
    ​Details  DOI 
  • 2019 | Journal Article | Erratum
    ​ ​Greven S, Kneib T. ​Correction to: On the behaviour of marginal and conditional AIC in linear mixed models​. ​​Biometrika. ​2019;. ​doi:10.1093/biomet/asz051. 
    ​Details  DOI 
  • 2019 | Preprint
    ​ ​Wiemann P, Kneib T. ​Using the Softplus Function to Construct Alternative Link Functions in Generalized Linear Models and Beyond​. ​​2019. 
    ​Details 
  • 2019 | Preprint
    ​ ​Hohberg M, Donat F, Marra G, Kneib T. ​Beyond unidimensional poverty analysis using distributional copula models for mixed ordered-continuous outcomes​. ​​2019. 
    ​Details 
  • 2019 | Journal Article
    ​ ​Filippou P, Kneib T, Marra G, Radice R. ​A trivariate additive regression model with arbitrary link functions and varying correlation matrix​. ​​Journal of Statistical Planning and Inference. ​2019;​199​:​​236​-248​. ​doi:10.1016/j.jspi.2018.07.002. 
    ​Details  DOI 
  • 2019 | Preprint
    ​ ​Gutleb DR, Roos C, Heistermann M, De Moor D, Kneib T, Noll A, et al. ​A multi-locus genetic risk score modulates social buffering of HPA axis activity in wild male primates​. ​​2019. 
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  • 2019 | Journal Article
    ​ ​Klein N, Kneib T. ​Directional bivariate quantiles: a robust approach based on the cumulative distribution function​. ​​Advances in Statistical Analysis. ​2019;. ​doi:10.1007/s10182-019-00355-3. 
    ​Details  DOI 
  • 2019 | Preprint
    ​ ​Stadlmann S, Kneib T. ​distreg.vis: Interactively visualizing distributional regression models​. ​​2019. 
    ​Details 
  • 2019 | Journal Article
    ​ ​Pollice A, Jona Lasinio G, Rossi R, Amato M, Kneib T, Lang S. ​Bayesian measurement error correction in structured additive distributional regression with an application to the analysis of sensor data on soil–plant variability​. ​​Stochastic Environmental Research and Risk Assessment. ​2019;​33​(3):​​747​-763​. ​doi:10.1007/s00477-019-01667-1. 
    ​Details  DOI 
  • 2019 | Journal Article
    ​ ​Otto-Sobotka F, Salvati N, Ranalli MG, Kneib T. ​Adaptive semiparametric M-quantile regression​. ​​Econometrics and Statistics. ​2019;​11​:​​116​-129​. ​doi:10.1016/j.ecosta.2019.03.001. 
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  • 2019 | Journal Article | 
    ​ ​Signer J, Filla M, Schoneberg S, Kneib T, Bufka L, Belotti E, et al. ​Rocks rock: the importance of rock formations as resting sites of the Eurasian lynx Lynx lynx​. ​​Wildlife Biology. ​2019;​2019​(1). ​doi:10.2981/wlb.00489. 
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  • 2019 | Journal Article | Research Paper | 
    ​ ​Darras KFA, Corre MD, Formaglio G, Tjoa A, Potapov A, Brambach F, et al. ​Reducing Fertilizer and Avoiding Herbicides in Oil Palm Plantations - Ecological and Economic Valuations​. ​​Frontiers in Forests and Global Change. ​2019;​2​. ​doi:10.3389/ffgc.2019.00065. 
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  • 2019 | Journal Article | 
    ​ ​Säfken B, Kneib T. ​Conditional covariance penalties for mixed models​. ​​Scandinavian Journal of Statistics. ​2019;​47​(3):​​990​-1010​. ​doi:10.1111/sjos.12437. 
    ​Details  DOI 
  • 2019 | Journal Article
    ​ ​Espasandín-Domínguez J, Cadarso-Suárez C, Kneib T, Marra G, Klein N, Radice R, et al. ​Assessing the relationship between markers of glycemic control through flexible copula regression models​. ​​Statistics in Medicine. ​2019;​38​(27):​​5161​-5181​. ​doi:10.1002/sim.8358. 
    ​Details  DOI  PMID  PMC 
  • 2019 | Journal Article
    ​ ​Martini J, Rosales F, Ha, Ngoc-Thuy, Heise J, Wimmer V, Kneib T. ​Lost in Translation: On the Problem of Data Coding in Penalized Whole Genome Regression with Interactions​. ​​G3: Genes, Genomes, Genetics. ​2019;​9​(4):​​1117​-1129​. ​doi:10.1534/g3.118.200961. 
    ​Details  DOI  PMID  PMC 
  • 2019 | Journal Article
    ​ ​Klein N, Kneib T, Marra G, Radice R, Rokicki S, McGovern ME. ​Mixed binary-continuous copula regression models with application to adverse birth outcomes​. ​​Statistics in Medicine. ​2019;​38​(3):​​413​-436​. ​doi:10.1002/sim.7985. 
    ​Details  DOI  PMID  PMC 
  • 2018 | Preprint
    ​ ​Hohberg M, Pütz P, Kneib T. ​Generalized additive models for location, scale and shape for program evaluation: ​A guide to practice​. ​​2018. 
    ​Details 
  • 2018 | Preprint
    ​ ​Säfken B, Rügamer D, Kneib T, Greven S. ​Conditional Model Selection in Mixed-Effects Models with cAIC4​. ​​2018. 
    ​Details  arXiv 
  • 2018 | Preprint
    ​ ​Hambuckers J, Groll A, Kneib T. ​Understanding the Economic Determinants of the Severity of Operational Losses: A Regularized Generalized Pareto Regression Approach​. ​​2018. 
    ​Details 
  • 2018 | Journal Article
    ​ ​Umlauf N, Kneib T. ​A primer on Bayesian distributional regression​. ​​Statistical Modelling. ​2018;​18​(3-4):​​219​-247​. ​doi:10.1177/1471082X18759140. 
    ​Details  DOI 
  • 2018 | Journal Article
    ​ ​Hambuckers J, Groll A, Kneib T. ​Understanding the economic determinants of the severity of operational losses: A regularized generalized Pareto regression approach​. ​​Journal of Applied Econometrics. ​2018;​33​(6):​​898​-935​. ​doi:10.1002/jae.2638. 
    ​Details  DOI 
  • 2018 | Journal Article | Editorial Contribution (Editorial, Introduction, Epilogue)
    ​ ​Groll A, Kneib T, Mayr A. ​Editorial 'Bridging the gap between methodology and applications: Tutorials on semiparametric regression'​. ​​Statistical Modelling. ​2018;​18​(3-4):​​199​-202​. ​doi:10.1177/1471082X18761252. 
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  • 2018 | Journal Article | Editorial Contribution (Editorial, Introduction, Epilogue)
    ​ ​He X, Kneib T, Lamarche C, Wang L. ​Editorial: Special issue on quantile regression and semiparametric methods​. ​​Econometrics and Statistics. ​2018;​8​:​​1​-2​. ​doi:10.1016/j.ecosta.2018.09.002. 
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  • 2018 | Journal Article
    ​ ​Groll A, Kneib T, Mayr A, Schauberger G. ​On the dependency of soccer scores – a sparse bivariate Poisson model for the UEFA European football championship 2016​. ​​Journal of Quantitative Analysis in Sports. ​2018;​14​(2):​​65​-79​. ​doi:10.1515/jqas-2017-0067. 
    ​Details  DOI 
  • 2018 | Preprint
    ​ ​Herbst H, Minnich A, Herminghaus S, Kneib T, Wacker B, Schlüter JC. ​A Behavioral Economic Perspective on Demand Responsive Transportation​. ​​2018. 
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  • 2018 | Journal Article
    ​ ​Ríos-Pena L, Kneib T, Cadarso-Suárez C, Klein N, Marey-Pérez M. ​Studying the occurrence and burnt area of wildfires using zero-one-inflated structured additive beta regression​. ​​Environmental Modelling & Software. ​2018;​110​:​​107​-118​. ​doi:10.1016/j.envsoft.2018.03.008. 
    ​Details  DOI 
  • 2018 | Journal Article
    ​ ​Michaelis P, Klein N, Kneib T. ​Bayesian Multivariate Distributional Regression With Skewed Responses and Skewed Random Effects​. ​​Journal of Computational and Graphical Statistics. ​2018;​27​(3):​​602​-611​. ​doi:10.1080/10618600.2017.1395343. 
    ​Details  DOI 
  • 2018 | Journal Article
    ​ ​Baumgartner B, Guhl D, Kneib T, Steiner WJ. ​Flexible estimation of time-varying effects for frequently purchased retail goods: a modeling approach based on household panel data​. ​​OR Spectrum. ​2018;​40​(4):​​837​-873​. ​doi:10.1007/s00291-018-0530-6. 
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  • 2018 | Journal Article
    ​ ​Thaden H, Kneib T. ​Structural Equation Models for Dealing With Spatial Confounding​. ​​The American Statistician. ​2018;​72​(3):​​239​-252​. ​doi:10.1080/00031305.2017.1305290. 
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  • 2018 | Journal Article
    ​ ​Hambuckers J, Kneib T, Langrock R, Silbersdorff A. ​A Markov-switching generalized additive model for compound Poisson processes, with applications to operational loss models​. ​​Quantitative Finance. ​2018;​18​(10):​​1679​-1698​. ​doi:10.1080/14697688.2017.1417625. 
    ​Details  DOI 
  • 2018 | Journal Article
    ​ ​Guhl D, Baumgartner B, Kneib T, Steiner WJ. ​Estimating time-varying parameters in brand choice models: A semiparametric approach​. ​​International Journal of Research in Marketing. ​2018;​35​(3):​​394​-414​. ​doi:10.1016/j.ijresmar.2018.03.003. 
    ​Details  DOI 
  • 2018 | Journal Article
    ​ ​Espasandín-Domínguez J, Benítez-Estévez AJ, Cadarso-Suárez C, Kneib T, Barreiro-Martínez T, Casas-Méndez B, et al. ​Geographical differences in blood potassium detected using a structured additive distributional regression model​. ​​Spatial Statistics. ​2018;​24​:​​1​-13​. ​doi:10.1016/j.spasta.2018.03.001. 
    ​Details  DOI 
  • 2018 | Journal Article | 
    ​ ​Hohberg M, Landau K, Kneib T, Klasen S, Zucchini W. ​Vulnerability to poverty revisited: Flexible modeling and better predictive performance​. ​​The Journal of Economic Inequality. ​2018;:​​1​-16​. ​doi:10.1007/s10888-017-9374-6. 
    ​Details  DOI 
  • 2018 | Journal Article
    ​ ​Silbersdorff A, Lynch J, Klasen S, Kneib T. ​Reconsidering the income-health relationship using distributional regression​. ​​Health Economics. ​2018;​27​(7):​​1074​-1088​. ​doi:10.1002/hec.3656. 
    ​Details  DOI  PMID  PMC 
  • 2017 | Preprint
    ​ ​Hambuckers J, Kneib T, Langrock R, Silbersdorff A. ​A Markov-Switching Generalized Additive Model for Compound Poisson Processes, with Applications to Operational Losses Models​. ​​2017. 
    ​Details 
  • 2017 | Preprint
    ​ ​Adam T, Mayr A, Kneib T. ​Gradient boosting in Markov-switching generalized additive models for location, scale and shape​. ​​2017. 
    ​Details  arXiv 
  • 2017 | Preprint
    ​ ​Mascarenhas A, Marques F, Silva S, Gouveia S, Alves M, Virella D, et al. ​Determinants of the Variability of Oxygen Saturation during the First Minutes of Life of Term Neonates​. ​​2017. 
    ​Details 
  • 2017 | Journal Article
    ​ ​Spiegel E, Kneib T, Otto-Sobotka F. ​Generalized additive models with flexible response functions​. ​​Statistics and Computing. ​2017;​29​(1):​​123​-138​. ​doi:10.1007/s11222-017-9799-6. 
    ​Details  DOI 
  • 2017 | Journal Article
    ​ ​Pütz P, Kneib T. ​A penalized spline estimator for fixed effects panel data models​. ​​Advances in Statistical Analysis. ​2017;​102​(2):​​145​-166​. ​doi:10.1007/s10182-017-0296-1. 
    ​Details  DOI 
  • 2017 | Journal Article | 
    ​ ​Spiegel E, Sobotka F, Kneib T. ​Model selection in semiparametric expectile regression​. ​​Electronic Journal of Statistics. ​2017;​11​(2):​​3008​-3038​. ​doi:10.1214/17-EJS1307. 
    ​Details  DOI 
  • 2017 | Journal Article | 
    ​ ​Idzalika R, Kneib T, Martinez-Zarzoso I. ​The effect of income on democracy revisited a flexible distributional approach​. ​​Empirical Economics. ​2017;​56​(4):​​1207​-1230​. ​doi:10.1007/s00181-017-1390-7. 
    ​Details  DOI 
  • 2017 | Journal Article | 
    ​ ​Langrock R, Kneib T, Glennie R, Michelot T. ​Markov-switching generalized additive models​. ​​Statistics and Computing. ​2017;​27​(1):​​259​-270​. ​doi:10.1007/s11222-015-9620-3. 
    ​Details  DOI 
  • 2017 | Journal Article
    ​ ​Ríos-Pena L, Kneib T, Cadarso-Suárez C, Marey-Pérez M. ​Predicting the occurrence of wildfires with binary structured additive regression models​. ​​Journal of Environmental Management. ​2017;​187​:​​154​-165​. ​doi:10.1016/j.jenvman.2016.11.044. 
    ​Details  DOI  PMID  PMC 
  • 2017 | Journal Article | Editorial Contribution (Editorial, Introduction, Epilogue)
    ​ ​Cadarso Suárez C, Klein N, Kneib T, Molenberghs G, Rizopoulos D. ​Editorial "Joint modeling of longitudinal and time-to-event data and beyond"​. ​​Biometrical Journal. ​2017;​59​(6):​​1101​-1103​. ​doi:10.1002/bimj.201700180. 
    ​Details  DOI  PMID  PMC 
  • 2017 | Journal Article
    ​ ​Mamouridis V, Klein N, Kneib T, Cadarso Suarez C, Maynou F. ​Structured additive distributional regression for analysing landings per unit effort in fisheries research​. ​​Mathematical Biosciences. ​2017;​283​:​​145​-154​. ​doi:10.1016/j.mbs.2016.11.016. 
    ​Details  DOI  PMID  PMC 
  • 2017 | Journal Article | 
    ​ ​Winter A, Kneib T, Wasylow C, Reinhardt L, Henke, Rolf-Peter, Engels S, et al. ​Updated Nomogram Incorporating Percentage of Positive Cores to Predict Probability of Lymph Node Invasion in Prostate Cancer Patients Undergoing Sentinel Lymph Node Dissection​. ​​Journal of Cancer. ​2017;​8​(14):​​2692​-2698​. ​doi:10.7150/jca.20409. 
    ​Details  DOI  PMID  PMC 
  • 2016 | Journal Article
    ​ ​März A, Klein N, Kneib T, Mußhoff O. ​Analysing farmland rental rates using Bayesian geoadditive quantile regression​. ​​European Review of Agricultural Economics. ​2016;​43​(4):​​663​-698​. ​doi:10.1093/erae/jbv028. 
    ​Details  DOI 
  • 2016 | Journal Article
    ​ ​Kneib T. ​Smoothing Parameter and Model Selection for General Smooth Models Comment​. ​​Journal of the American Statistical Association. ​2016;​111​(516):​​1563​-1565​. ​doi:10.1080/01621459.2016.1250576. 
    ​Details  DOI  WoS 
  • 2016 | Journal Article | Research Paper
    ​ ​Sohn A, Klein N, Kneib T. ​A Semiparametric Analysis of Conditional Income Distributions​. ​​Schmollers Jahrbuch. ​2016;​135​(1):​​13​-22​. ​doi:10.3790/schm.135.1.13. 
    ​Details  DOI 
  • 2016 | Journal Article | Erratum | 
    ​ ​Klein N, Kneib T, Lang S, Sohn A. ​Correction: Bayesian structured additive distributional regression with an application to regional income inequality in Germany​. ​​The Annals of Applied Statistics. ​2016;​10​(2):​​1135​-1136​. ​doi:10.1214/16-AOAS922. 
    ​Details  DOI 
  • 2016 | Journal Article | 
    ​ ​Álvaro-Meca A, Jiménez-Sousa MA, Boyer A, Medrano J, Reulen H, Kneib T, et al. ​Impact of chronic hepatitis C on mortality in cirrhotic patients admitted to intensive-care unit​. ​​BMC Infectious Diseases. ​2016;​16​(1). ​doi:10.1186/s12879-016-1448-8. 
    ​Details  DOI 
  • 2016 | Journal Article | 
    ​ ​Tahden M, Manitz J, Baumgardt K, Fell G, Kneib T, Hegasy G. ​Epidemiological and Ecological Characterization of the EHEC O104:H4 Outbreak in Hamburg, Germany, 2011​. ​​PLOS ONE. ​2016;​11​(10). ​doi:10.1371/journal.pone.0164508. 
    ​Details  DOI  PMID  PMC 
  • 2016 | Journal Article
    ​ ​Sennhenn-Reulen H, Kneib T. ​Structured fusion lasso penalized multi-state models​. ​​Statistics in Medicine. ​2016;​35​(25):​​4637​-4659​. ​doi:10.1002/sim.7017. 
    ​Details  DOI  PMID  PMC 
  • 2015 | Review
    ​ ​Kneib T. ​Applied Statistical Inference: Likelihood and Bayes. L.Held and D.Sabanés Bové (2014). Heidelberg: Springer. 376 pages, ISBN: 3642378862​ [book review]. ​​Biometrical Journal. ​2015;​57​(2):​​362​-363​. ​Review of: Held L, Sabanés Bové D​, editors. ​Applied Statistical Inference: Likelihood and Bayes. doi:10.1002/bimj.201400209. 
    ​Details  DOI 
  • 2015 | Journal Article
    ​ ​Waltrup LS, Sobotka F, Kneib T, Kauermann G. ​Expectile and quantile regression-David and Goliath?​. ​​Statistical Modelling. ​2015;​15​(5):​​433​-456​. ​doi:10.1177/1471082X14561155. 
    ​Details  DOI  WoS 
  • 2015 | Journal Article | 
    ​ ​Umlauf N, Adler D, Kneib T, Lang S, Zeileis A. ​Structured Additive Regression Models: An R Interface to BayesX​. ​​Journal of Statistical Software. ​2015;​63​(21):​​1​-46​. ​doi:10.18637/jss.v063.i21. 
    ​Details  DOI 
  • 2015 | Journal Article | 
    ​ ​Ríos-Pena L, Cadarso-Suárez C, Kneib T, Pérez M. ​Applying Binary Structured Additive Regression (STAR) for Predicting Wildfire in Galicia, Spain​. ​​Procedia Environmental Sciences. ​2015;​27​:​​123​-126​. ​doi:10.1016/j.proenv.2015.07.121. 
    ​Details  DOI 
  • 2015 | Journal Article | 
    ​ ​Winter A, Kneib T, Rohde M, Henke, Rolf-Peter, Wawroschek F. ​First Nomogram Predicting the Probability of Lymph Node Involvement in Prostate Cancer Patients Undergoing Radioisotope Guided Sentinel Lymph Node Dissection​. ​​Urologia Internationalis. ​2015;​95​(4):​​422​-428​. ​doi:10.1159/000431182. 
    ​Details  DOI  PMID  PMC 
  • 2014 | Journal Article
    ​ ​Manitz J, Kneib T, Schlather M, Helbing D, Brockmann D. ​Origin Detection During Food-borne Disease Outbreaks - A Case Study of the 2011 EHEC/HUS Outbreak in Germany​. ​​PLoS Currents. ​2014;. ​doi:10.1371/currents.outbreaks.f3fdeb08c5b9de7c09ed9cbcef5f01f2. 
    ​Details  DOI  PMID  PMC 
  • 2014 | Report
    ​ ​Sohn A, Klein N, Kneib T. ​A New Semiparametric Approach to Analysing Conditional Income Distributions​. ​​2014. doi: https://doi.org/10.2139/ssrn.2404335 
    ​Details  DOI 
  • 2014 | Journal Article | Research Paper | 
    ​ ​Saefken B, Kneib T, van Waveren, Clara-Sophie, Greven S. ​A unifying approach to the estimation of the conditional Akaike information in generalized linear mixed models​. ​​Electronic Journal of Statistics. ​2014;​8​(1):​​201​-225​. ​doi:10.1214/14-EJS881. 
    ​Details  DOI 
  • 2014 | Journal Article | 
    ​ ​Helms, Hans-Joachim, Benda N, Zinserling J, Kneib T, Friede T. ​Spline-based procedures for dose-finding studies with active control​. ​​Statistics in Medicine. ​2014;​34​(2):​​232​-248​. ​doi:10.1002/sim.6320. 
    ​Details  DOI  PMID  PMC 
  • 2014 | Journal Article | 
    ​ ​Freytag S, Manitz J, Schlather M, Kneib T, Amos CI, Risch A, et al. ​A Network-Based Kernel Machine Test for the Identification of Risk Pathways in Genome-Wide Association Studies​. ​​Human Heredity. ​2014;​76​(2):​​64​-75​. ​doi:10.1159/000357567. 
    ​Details  DOI  PMID  PMC 
  • 2014 | Journal Article
    ​ ​Bühlmann P, Gertheiss J, Hieke S, Kneib T, Ma S, Schumacher M, et al. ​Discussion of "The Evolution of Boosting Algorithms" and "Extending Statistical Boosting"​. ​​Methods of Information in Medicine. ​2014;​53​(6):​​436​-445​. ​doi:10.3414/13100122. 
    ​Details  DOI  PMID  PMC  WoS 
  • 2013 | Preprint
    ​ ​Langrock R, Kneib T, Sohn A, DeRuiter S. ​Nonparametric inference in hidden Markov models using P-splines​. ​​2013. 
    ​Details  arXiv 
  • 2013 | Preprint
    ​ ​Langrock R, Michelot T, Sohn A, Kneib T. ​Semiparametric stochastic volatility modelling using penalized splines​. ​​2013. 
    ​Details  arXiv 
  • 2013 | Journal Article
    ​ ​Rodríguez-Girondo M, Kneib T, Cadarso-Suárez C, Abu-Assi E. ​Model building in nonproportional hazard regression​. ​​Statistics in Medicine. ​2013;​32​(30):​​5301​-5314​. ​doi:10.1002/sim.5961. 
    ​Details  DOI 
  • 2013 | Journal Article
    ​ ​Kneib T. ​Rejoinder​. ​​Statistical Modelling. ​2013;​13​(4):​​373​-385​. ​doi:10.1177/1471082X13494531. 
    ​Details  DOI  WoS 
  • 2013 | Journal Article | 
    ​ ​Kneib T. ​Beyond mean regression​. ​​Statistical Modelling. ​2013;​13​(4):​​275​-303​. ​doi:10.1177/1471082X13494159. 
    ​Details  DOI  WoS 
  • 2013 | Journal Article | 
    ​ ​Yue YR, Lang S, Flexeder C, Waldmann E, Kneib T. ​Bayesian semiparametric additive quantile regression​. ​​Statistical Modelling. ​2013;​13​(3):​​223​-252​. ​doi:10.1177/1471082x13480650. 
    ​Details  DOI 
  • 2013 | Journal Article | 
    ​ ​Freytag S, Bickeböller H, Amos CI, Kneib T, Schlather M. ​A Novel Kernel for Correcting Size Bias in the Logistic Kernel Machine Test with an Application to Rheumatoid Arthritis​. ​​Human Heredity. ​2013;​74​(2):​​97​-108​. ​doi:10.1159/000347188. 
    ​Details  DOI  PMID  PMC 
  • 2012 | Journal Article
    ​ ​Mehr M, Brandl R, Kneib T, Müller J. ​The effect of bark beetle infestation and salvage logging on bat activity in a national park​. ​​Biodiversity and Conservation. ​2012;​21​(11):​​2775​-2786​. ​doi:10.1007/s10531-012-0334-y. 
    ​Details  DOI 
  • 2012 | Conference Abstract
    ​ ​Freytag S, Amos CI, Bickeböller H, Kneib T, Schlather M. ​Novel Kernel Function in the Logistic Kernel Machine Test for Pathways in GWA Studies​. ​ Annual Meeting of the International-Genetic-Epidemiology-Society (IGES); ​Stevenson, WA. ​Genetic Epidemiology. ​2012;​36​(7)
    ​Details  WoS 
  • 2011 | Journal Article | 
    ​ ​Sobotka F, Kauermann G, Schulze Waltrup L, Kneib T. ​On confidence intervals for semiparametric expectile regression​. ​​Statistics and Computing. ​2011;​23​(2):​​135​-148​. ​doi:10.1007/s11222-011-9297-1. 
    ​Details  DOI 

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