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. 
    ​Details  DOI 
  • 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. 
    ​Details  DOI 
  • 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. 
    ​Details 
  • 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. 
    ​Details  DOI 
  • 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 | Preprint
    ​ ​Voncken L, Kneib T, Albers C, Umlauf N, Timmerman M. ​Bayesian Gaussian distributional regression models for more efficient norm estimation​. ​​2019. Available from: https://doi.org/10.31234/osf.io/7j8ym​
    ​Details  DOI 
  • 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. 
    ​Details  DOI 
  • 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. 
    ​Details  DOI 
  • 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. 
    ​Details  DOI 
  • 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. 
    ​Details  DOI 
  • 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. 
    ​Details 
  • 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. 
    ​Details  DOI 
  • 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. 
    ​Details  DOI 
  • 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
    ​ ​Thaden H, Pata MP, Klein N, Cadarso-Suárez C, Kneib T. ​Integrating multivariate conditionally autoregressive spatial priors into recursive bivariate models for analyzing environmental sensitivity of mussels​. ​​Spatial Statistics. ​2017;​22​:​​419​-433​. ​doi:10.1016/j.spasta.2017.07.005. 
    ​Details  DOI 
  • 2017 | Journal Article
    ​ ​Duarte E, de Sousa B, Cadarso-Suárez C, Kneib T, Rodrigues V. ​Exploring risk factors in breast cancer screening program data using structured geoadditive models with high order interaction​. ​​Spatial Statistics. ​2017;​22​:​​403​-418​. ​doi:10.1016/j.spasta.2017.07.004. 
    ​Details  DOI 
  • 2017 | Journal Article
    ​ ​Vonrüti M, Spasojevic A, Nölke N, Kneib T, Kleinn C. ​Comparing canopy leaf temperature of three Central European tree species based on simultaneous confidence bands for penalized splines​. ​​Environmental and Ecological Statistics. ​2017;​24​(3):​​385​-398​. ​doi:10.1007/s10651-017-0375-1. 
    ​Details  DOI 
  • 2017 | Journal Article
    ​ ​Waldmann E, Sobotka F, Kneib T. ​Bayesian regularisation in geoadditive expectile regression​. ​​Statistics and Computing. ​2017;​27​(6):​​1539​-1553​. ​doi:10.1007/s11222-016-9703-9. 
    ​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
    ​ ​Duarte E, de Sousa B, Cadarso-Suárez C, Klein N, Kneib T, Rodrigues V. ​Studying the relationship between a woman's reproductive lifespan and age at menarche using a Bayesian multivariate structured additive distributional regression model​. ​​Biometrical journal. Biometrische Zeitschrift. ​2017;​59​(6):​​1232​-1246​. ​doi:10.1002/bimj.201600245. 
    ​Details  DOI  PMID  PMC 
  • 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
    ​ ​Taylor-Robinson D, Pressler T, Schmid M, Mayr A, Waldmann E, Klein N, et al. ​Boosting joint models for longitudinal and time-to-event data​. ​​Biometrical Journal. ​2017;​59​(6):​​1104-1121​-1121​. ​doi:10.1002/bimj.201600158. 
    ​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 
  • 2017 | Journal Article | 
    ​ ​Friedrichs S, Manitz J, Burger P, Amos CI, Risch A, Chang-Claude J, et al. ​Pathway-Based Kernel Boosting for the Analysis of Genome-Wide Association Studies​. ​​Computational and mathematical methods in medicine. ​2017;​2017​:​​6742763​-17​. ​doi:10.1155/2017/6742763. 
    ​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
    ​ ​Manitz J, Harbering J, Schmidt M, Kneib T, Schoebel A. ​Source estimation for propagation processes on complex networks with an application to delays in public transportation systems​. ​​Journal of the Royal Statistical Society. Series C, Applied statistics. ​2016;​66​(3):​​521​-536​. ​doi:10.1111/rssc.12176. 
    ​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 | Journal Article
    ​ ​Michelot T, Langrock R, Kneib T, King R. ​Maximum penalized likelihood estimation in semiparametric mark-recapture-recovery models​. ​​Biometrical Journal. ​2015;​58​(1):​​222​-239​. ​doi:10.1002/bimj.201400222. 
    ​Details  DOI 
  • 2015 | Journal Article
    ​ ​Klein N, Kneib T, Klasen S, Lang S. ​Bayesian structured additive distributional regression for multivariate responses​. ​​Journal of the Royal Statistical Society. Series C, Applied statistics. ​2015;​64​(4):​​569​-591​. ​doi:10.1111/rssc.12090. 
    ​Details  DOI 
  • 2015 | Journal Article
    ​ ​Waldmann E, Kneib T. ​Variational approximations in geoadditive latent Gaussian regression: mean and quantile regression​. ​​Statistics and Computing. ​2015;​25​(6):​​1247​-1263​. ​doi:10.1007/s11222-014-9480-2. 
    ​Details  DOI 
  • 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
    ​ ​Langrock R, Kneib T, Sohn A, Ruiter SL. ​Nonparametric inference in hidden Markov models using P-splines​. ​​Biometrics. ​2015;​71​(2):​​520​-528​. ​doi:10.1111/biom.12282. 
    ​Details  DOI 
  • 2015 | Journal Article
    ​ ​Klein N, Kneib T. ​Scale-Dependent Priors for Variance Parameters in Structured Additive Distributional Regression​. ​​Bayesian Analysis. ​2015;​11​(4):​​1071​-1106​. ​doi:10.1214/15-ba983. 
    ​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 | Preprint
    ​ ​Razen A, Brunauer W, Klein N, Kneib T, Lang S, Umlauf N. ​Statistical risk analysis for real estate collateral valuation using Bayesian distributional and quantile regression​. ​​2015. 
    ​Details 
  • 2015 | Preprint
    ​ ​März A, Klein N, Kneib T, Mußhoff O. ​Intergenerational social mobility in the United States: A multivariate analysis using Bayesian distributional regression​. ​​2015. 
    ​Details 
  • 2015 | Journal Article
    ​ ​Klein N, Kneib T, Lang S, Sohn A. ​Bayesian structured additive distributional regression with an application to regional income inequality in Germany​. ​​The Annals of Applied Statistics. ​2015;​9​(2):​​1024​-1052​. ​doi:10.1214/15-aoas823. 
    ​Details  DOI 
  • 2015 | Journal Article
    ​ ​Klein N, Kneib T. ​Simultaneous inference in structured additive conditional copula regression models: a unifying Bayesian approach​. ​​Statistics and Computing. ​2015;​26​(4):​​841​-860​. ​doi:10.1007/s11222-015-9573-6. 
    ​Details  DOI 
  • 2015 | Journal Article
    ​ ​Brezger A, Kneib T, Lang S. ​BayesX: Analyzing Bayesian Structured Additive Regression Models​. ​​Journal of Statistical Software. ​2015;​14​(11). ​doi:10.18637/jss.v014.i11. 
    ​Details  DOI 
  • 2015 | Journal Article
    ​ ​Reulen H, Kneib T. ​Boosting multi-state models​. ​​Lifetime Data Analysis. ​2015;​22​(2):​​241​-262​. ​doi:10.1007/s10985-015-9329-9. 
    ​Details  DOI 
  • 2015 | Journal Article | 
    ​ ​Buck C, Kneib T, Tkaczick T, Konstabel K, Pigeot I. ​Assessing opportunities for physical activity in the built environment of children: interrelation between kernel density and neighborhood scale​. ​​International Journal of Health Geographics. ​2015;​14​(1). ​doi:10.1186/s12942-015-0027-3. 
    ​Details  DOI 
  • 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 | Preprint
    ​ ​Hofner B, Kneib T, Hartl WH, Kuchenho H. ​Model Choice in Cox-Type Additive Hazard Regression Models with Time-Varying Effects​. ​​2014. 
    ​Details 
  • 2014 | Journal Article
    ​ ​Klein N, Denuit M, Lang S, Kneib T. ​Nonlife ratemaking and risk management with Bayesian generalized additive models for location, scale, and shape​. ​​Insurance: Mathematics and Economics. ​2014;​55​:​​225​-249​. ​doi:10.1016/j.insmatheco.2014.02.001. 
    ​Details  DOI 
  • 2014 | Conference Paper
    ​ ​Rodríguez-Álvarez MX, Kneib T, Cadarso-Suárez C. ​Semiparametric ROC Regression based on Conditional Transformation Models​. ​​Proceedings of the 29th Internacional Workshop on Statistical Modelling. 29th Internacional Workshop on Statistical Modelling; ​2014-07-14​ - 2014-07-18​; ​​Göttingen. Vol. 2. ​2014.  p. 145​-148​. ​
    ​Details 
  • 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 | Journal Article
    ​ ​Duarte E, Sousa B, Cadarso-Suarez C, Rodrigues V, Kneib T. ​Structured additive regression modeling of age of menarche and menopause in a breast cancer screening program​. ​​Biometrical Journal. ​2014;​56​(3):​​416​-427​. ​doi:10.1002/bimj.201200260. 
    ​Details  DOI 
  • 2014 | Journal Article
    ​ ​Wiesenfarth M, Hisgen CM, Kneib T, Cadarso-Suarez C. ​Bayesian Nonparametric Instrumental Variables Regression Based on Penalized Splines and Dirichlet Process Mixtures​. ​​Journal of Business & Economic Statistics. ​2014;​32​(3):​​468​-482​. ​doi:10.1080/07350015.2014.907092. 
    ​Details  DOI 
  • 2014 | Journal Article
    ​ ​Langrock R, Michelot T, Sohn A, Kneib T. ​Semiparametric stochastic volatility modelling using penalized splines​. ​​Computational Statistics. ​2014;​30​(2):​​517​-537​. ​doi:10.1007/s00180-014-0547-5. 
    ​Details  DOI 
  • 2014 | Journal Article
    ​ ​Konrath S, Fahrmeir L, Kneib T. ​Bayesian accelerated failure time models based on penalized mixtures of Gaussians: regularization and variable selection​. ​​AStA Advances in Statistical Analysis. ​2014;​99​(3):​​259​-280​. ​doi:10.1007/s10182-014-0240-6. 
    ​Details  DOI 
  • 2014 | Preprint
    ​ ​Oelker MR, Sobotka F, Klein N, Kneib T. ​Semiparametric Mode Regression​. ​​2014. 
    ​Details 
  • 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
    ​ ​Klein N, Kneib T, Lang S. ​Bayesian Generalized Additive Models for Location, Scale, and Shape for Zero-Inflated and Overdispersed Count Data​. ​​Journal of the American Statistical Association. ​2014;​110​(509):​​405​-419​. ​doi:10.1080/01621459.2014.912955. 
    ​Details  DOI 
  • 2014 | Journal Article
    ​ ​Rodríguez-Álvarez MX, Lee, Dae-Jin, Kneib T, Durbán M, Eilers P. ​Fast smoothing parameter separation in multidimensional generalized P-splines: the SAP algorithm​. ​​Statistics and Computing. ​2014;​25​(5):​​941​-957​. ​doi:10.1007/s11222-014-9464-2. 
    ​Details  DOI 
  • 2014 | Journal Article
    ​ ​Waldmann E, Kneib T. ​Bayesian bivariate quantile regression​. ​​Statistical Modelling. ​2014;​15​(4):​​326​-344​. ​doi:10.1177/1471082x14551247. 
    ​Details  DOI 
  • 2014 | Journal Article
    ​ ​Hofner B, Kneib T, Hothorn T. ​A unified framework of constrained regression​. ​​Statistics and Computing. ​2014;​26​(1-2):​​1​-14​. ​doi:10.1007/s11222-014-9520-y. 
    ​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 
  • 2014 | Journal Article
    ​ ​Winter A, Kneib T, Henke, Rolf-Peter, Wawroschek F. ​Sentinel lymph node dissection in more than 1200 prostate cancer cases: Rate and prediction of lymph node involvement depending on preoperative tumor characteristics​. ​​International Journal of Urology. ​2014;​21​(1):​​58​-63​. ​doi:10.1111/iju.12184. 
    ​Details  DOI  PMID  PMC 
  • 2013 | Preprint
    ​ ​Mamouridis V, Klein N, Kneib T, Cadarso C, Maynou F. ​Extended Additive Regression for Analysing LPUE Indices in Fishery Research​. ​​2013. 
    ​Details 
  • 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 | Conference Paper
    ​ ​Rodríguez-Álvarez MX, Kneib T, Lee, Dae-Jin, Durbán M. ​Fast algorithm for smoothing parameter selection in multidimensional P-splines​. ​​Proceedings of the 28th Internacional Workshop on Statistical Modelling. 28th Internacional Workshop on Statistical Modelling; ​2013-07-08​ - 2013-07-12​; ​​Palermo, Italy. ​2013.  p. 343​-349​. ​
    ​Details 
  • 2013 | Journal Article
    ​ ​Hillmann J, Kneib T, Koepcke L, Juárez Paz LM, Kretzberg J. ​Bivariate cumulative probit model for the comparison of neuronal encoding hypotheses​. ​​Biometrical Journal. ​2013;​56​(1):​​23​-43​. ​doi:10.1002/bimj.201200161. 
    ​Details  DOI 
  • 2013 | Journal Article
    ​ ​Scheipl F, Kneib T, Fahrmeir L. ​Penalized likelihood and Bayesian function selection in regression models​. ​​AStA Advances in Statistical Analysis. ​2013;​97​(4):​​349​-385​. ​doi:10.1007/s10182-013-0211-3. 
    ​Details  DOI 
  • 2013 | Book Chapter
    ​ ​Fahrmeir L, Kneib T, Lang S, Marx B. ​Quantile Regression​. ​​Regression: Modelle, Methoden und Anwendungen. ​3. ed. ​Berlin: ​Springer; ​2013. p. 597​-620​. ​doi:10.1007/978-3-642-34333-9_10. 
    ​Details  DOI 
  • 2013 | Book Chapter
    ​ ​Fahrmeir L, Kneib T, Lang S, Marx B. ​The Classical Linear Model​. ​​Regression: Modelle, Methoden und Anwendungen. ​3. ed. ​Berlin: ​Springer; ​2013. p. 73​-175​. ​doi:10.1007/978-3-642-34333-9_3. 
    ​Details  DOI 
  • 2013 | Journal Article
    ​ ​Álvaro-Meca A, Kneib T, Gil-Prieto R, Gil de Miguel A. ​Epidemiology of suicide in Spain, 1981–2008: A spatiotemporal analysis​. ​​Public Health. ​2013;​127​(4):​​380​-385​. ​doi:10.1016/j.puhe.2012.12.007. 
    ​Details  DOI 
  • 2013 | Monograph
    ​ ​Fahrmeir L, Kneib T, Lang S, Marx B. ​Regression: ​models, methods and applications​. ​​3. ed. ​Berlin: ​Springer; ​2013. ​XIV, 698 Seiten p. doi:10.1007/978-3-642-34333-9. 
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  • 2013 | Book Chapter
    ​ ​Konrath S, Fahrmeir L, Kneib T. ​Bayesian Smoothing, Shrinkage and Variable Selection in Hazard Regression​. ​In: Becker C, Fried R, Kuhnt S​, editors. ​Robustness and Complex Data Structures: Festschrift in Honour of Ursula Gather. ​Berlin, Heidelberg: ​Springer; ​2013. p. 149​-170​. ​doi:10.1007/978-3-642-35494-6_10. 
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  • 2013 | Journal Article
    ​ ​Sobotka F, Radice R, Marra G, Kneib T. ​Estimating the relationship between women's education and fertility in Botswana by using an instrumental variable approach to semiparametric expectile regression​. ​​Journal of the Royal Statistical Society. Series C, Applied statistics. ​2013;​62​(1):​​25​-45​. ​doi:10.1111/j.1467-9876.2012.01050.x. 
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  • 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. 
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  • 2013 | Book Chapter
    ​ ​Fahrmeir L, Kneib T, Lang S, Marx B. ​Mixed Models​. ​​Regression: Modelle, Methoden und Anwendungen. ​3. ed. ​Berlin: ​Springer; ​2013. p. 349​-412​. ​doi:10.1007/978-3-642-34333-9_7. 
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  • 2013 | Book Chapter
    ​ ​Fahrmeir L, Kneib T, Lang S, Marx B. ​Regression Models: Modelle, Methoden und Anwendungen​. ​In: Fahrmeir L, Kneib T, Lang S​, editors. ​Regression: Modelle, Methoden und Anwendungen. ​Berlin, Heidelberg: ​Springer; ​2013. p. 21​-72​. ​doi:10.1007/978-3-642-34333-9_2. 
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  • 2013 | Book Chapter
    ​ ​Fahrmeir L, Kneib T, Lang S. ​Bayesian Multilevel Models​. ​In: Scott MA, Simonoff JS, Marx BD​, editors. ​The SAGE Handbook of Multilevel Modeling. ​London: ​SAGE Publications Ltd; ​2013. p. 53​-72​. ​doi:10.4135/9781446247600.n4. 
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  • 2013 | Journal Article
    ​ ​Hothorn T, Kneib T, Bühlmann P. ​Conditional transformation models​. ​​Journal of the Royal Statistical Society: Series B (Statistical Methodology). ​2013;​76​(1):​​3​-27​. ​doi:10.1111/rssb.12017. 
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  • 2013 | Preprint
    ​ ​Alvaro-Meca A, Reulen H, Kneib T, Rodriguez-Gijon L, Gil de Miguel A, Resino S. ​Risk of hospital readmission and death among HIV infected patients with tuberculosis. A recurrent event analysis​. ​​2013. 
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  • 2013 | Journal Article
    ​ ​Kneib T. ​Rejoinder​. ​​Statistical Modelling. ​2013;​13​(4):​​373​-385​. ​doi:10.1177/1471082X13494531. 
    ​Details  DOI  WoS 
  • 2013 | Book Chapter
    ​ ​Fahrmeir L, Kneib T, Lang S, Marx B. ​Nonparametric Regression​. ​​Regression: Modelle, Methoden und Anwendungen. ​3. ed. ​Berlin: ​Springer; ​2013. p. 413​-533​. ​doi:10.1007/978-3-642-34333-9_8. 
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  • 2013 | Book Chapter
    ​ ​Fahrmeir L, Kneib T, Lang S, Marx B. ​Generalized Linear Models​. ​​Regression: Modelle, Methoden und Anwendungen. ​3. ed. ​Berlin: ​Springer; ​2013. p. 269​-324​. ​doi:10.1007/978-3-642-34333-9_5. 
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  • 2013 | Book Chapter
    ​ ​Fahrmeir L, Kneib T, Lang S, Marx B. ​Extensions of the Classical Linear Model​. ​​Regression: Modelle, Methoden und Anwendungen. ​3. ed. ​Berlin: ​Springer; ​2013. p. 177​-267​. ​doi:10.1007/978-3-642-34333-9_4. 
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  • 2013 | Book Chapter
    ​ ​Fahrmeir L, Kneib T, Lang S, Marx B. ​Categorical Regression Models​. ​​Regression: Modelle, Methoden und Anwendungen. ​3. ed. ​Berlin: ​Springer; ​2013. p. 325​-347​. ​doi:10.1007/978-3-642-34333-9_6. 
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  • 2013 | Book Chapter
    ​ ​Fahrmeir L, Kneib T, Lang S, Marx B. ​Structured Additive Regression​. ​​Regression: Modelle, Methoden und Anwendungen. ​3. ed. ​Berlin: ​Springer; ​2013. p. 535​-595​. ​doi:10.1007/978-3-642-34333-9_9. 
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  • 2013 | Journal Article | 
    ​ ​Kneib T. ​Beyond mean regression​. ​​Statistical Modelling. ​2013;​13​(4):​​275​-303​. ​doi:10.1177/1471082X13494159. 
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  • 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 | Review | 
    ​ ​Kneib T. ​Gerhard Tutz: „Regression for Categorical Data“​ [book review]. ​​Jahresbericht der Deutschen Mathematiker-Vereinigung. ​2013;​115​(1):​​51​-55​. ​Review of: Tutz G. ​Regression for Categorical Data. doi:10.1365/s13291-013-0058-2. 
    ​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
    ​ ​Winter A, Kneib T, Schatke N, Rohde M, Henke, Rolf-Peter, Wawroschek F. ​181 FIRST SENTINEL BASED NOMOGRAM PREDICTING THE PROBABILITY OF LYMPH NODE INVOLVEMENT IN PROSTATE CANCER PATIENTS UNDERGOING RADIO GUIDED LYMPH NODE DISSECTION AND RADICAL PROSTATECTOMY​. ​​Journal of Urology. ​2012;​187​(4S). ​doi:10.1016/j.juro.2012.02.233. 
    ​Details  DOI 
  • 2012 | Journal Article
    ​ ​Scheipl F, Fahrmeir L, Kneib T. ​Spike-and-Slab Priors for Function Selection in Structured Additive Regression Models​. ​​Journal of the American Statistical Association. ​2012;​107​(500):​​1518​-1532​. ​doi:10.1080/01621459.2012.737742. 
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  • 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. 
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  • 2012 | Journal Article
    ​ ​Heinzl F, Fahrmeir L, Kneib T. ​Additive mixed models with Dirichlet process mixture and P-spline priors​. ​​AStA Advances in Statistical Analysis. ​2012;​96​(1):​​47​-68​. ​doi:10.1007/s10182-011-0161-6. 
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  • 2012 | Journal Article
    ​ ​Álvaro-Meca A, Kneib T, Prieto RG, Miguel ÁG. ​Impact of comorbidities and surgery on health related transitions in pancreatic cancer admissions: A multi state model​. ​​Cancer Epidemiology. ​2012;​36​(2):​​e142​-e146​. ​doi:10.1016/j.canep.2011.12.005. 
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  • 2012 | Journal Article
    ​ ​Freytag S, Amos CI, Bickeböller H, Kneib T, Schlather M. ​Novel Kernel for Correcting Significance Bias in the Logistic Kernel Machine Test with an Application to Rheumatoid Arthritis​. ​​Human Heredity. ​2012;​74​:​​97​-108​. ​
    ​Details 
  • 2012 | Preprint
    ​ ​Sobotka F, Schnabel S, Schulze Waltrup L, Kauermann G, Kneib T. ​expectreg: An R Package for Expectile Regression​. ​​2012. 
    ​Details 
  • 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 
  • 2012 | Journal Article
    ​ ​Flores C, Rodriguez-Girondo M, Cadarso-Suarez C, Kneib T, Gomez G, Casanova L. ​Flexible Geoadditive Survival analysis of Non-Hodgkin Lymphoma in Peru​. ​​SORT. ​2012;​36​:​​221​-230​. ​
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  • 2012 | Journal Article
    ​ ​Kneib T. ​Discussion on "Analysing visual receptive fields through generalised additive models with interactions" by Rodríguez-Álvarez, M. X., Cadarso-Suárez, C. and González, F.​. ​​SORT - Statistics and Operations Research Transactions. ​2012;​36​:​​37​-38​. ​
    ​Details 
  • 2012 | Journal Article
    ​ ​Hofner B, Hothorn T, Kneib T. ​Variable selection and model choice in structured survival models​. ​​Computational Statistics. ​2012;​28​(3):​​1079​-1101​. ​doi:10.1007/s00180-012-0337-x. 
    ​Details  DOI 
  • 2012 | Journal Article
    ​ ​Lang S, Umlauf N, Wechselberger P, Harttgen K, Kneib T. ​Multilevel structured additive regression​. ​​Statistics and Computing. ​2012;​24​(2):​​223​-238​. ​doi:10.1007/s11222-012-9366-0. 
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  • 2012 | Journal Article
    ​ ​Mayr A, Fenske N, Hofner B, Kneib T, Schmid M. ​Generalized additive models for location, scale and shape for high dimensional data-a flexible approach based on boosting​. ​​Journal of the Royal Statistical Society. Series C, Applied statistics. ​2012;​61​(3):​​403​-427​. ​doi:10.1111/j.1467-9876.2011.01033.x. 
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  • 2011 | Book Chapter
    ​ ​Kneib T, Fahrmeir L. ​A Space-Time Study on Forest Health​. ​In: Chandler RE, Scott M​, editors. ​Statistical Methods for Trend Detection and Analysis in the Environmental Sciences. ​Wiley; ​2011. doi:10.1002/9781119991571. 
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  • 2011 | Journal Article
    ​ ​Hofner B, Kneib T, Hartl W, Küchenhoff H. ​Building Cox-type structured hazard regression models with time-varying effects​. ​​Statistical Modelling. ​2011;​11​(1):​​3​-24​. ​doi:10.1177/1471082x1001100102. 
    ​Details  DOI 
  • 2011 | Journal Article
    ​ ​Güthlin D, Knauer F, Kneib T, Küchenhoff H, Kaczensky P, Rauer G, et al. ​Estimating habitat suitability and potential population size for brown bears in the Eastern Alps​. ​​Biological Conservation. ​2011;​144​(5):​​1733​-1741​. ​doi:10.1016/j.biocon.2011.03.010. 
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  • 2011 | Journal Article
    ​ ​Rocker D, Kisand V, Scholz-Böttcher B, Kneib T, Lemke A, Rullkötter J, et al. ​Differential decomposition of humic acids by marine and estuarine bacterial communities at varying salinities​. ​​Biogeochemistry. ​2011;​111​(1-3):​​331​-346​. ​doi:10.1007/s10533-011-9653-4. 
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  • 2011 | Preprint
    ​ ​Otto-Sobotka F, Kneib T, Schnabel S, Eilers P. ​What to expect – an R vignette for expectreg​. ​​2011. 
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  • 2011 | Journal Article
    ​ ​Fenske N, Kneib T, Hothorn T. ​Identifying Risk Factors for Severe Childhood Malnutrition by Boosting Additive Quantile Regression​. ​​Journal of the American Statistical Association. ​2011;​106​(494):​​494​-510​. ​doi:10.1198/jasa.2011.ap09272. 
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  • 2011 | Journal Article
    ​ ​Pata MP, Kneib T, Cadarso-Suarez C, Lustres-Pérez V, Fernández-Pulpeiro E. ​Categorical structured additive regression for assessing habitat suitability in the spatial distribution of mussel seed abundance​. ​​Environmetrics. ​2011;​23​(1):​​75​-84​. ​doi:10.1002/env.1140. 
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  • 2011 | Monograph
    ​ ​Fahrmeir L, Kneib T. ​Bayesian Smoothing and Regression for Longitudinal, Spatial and Event History Data​. ​​Oxford University Press; ​2011. doi:10.1093/acprof:oso/9780199533022.001.0001. 
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  • 2011 | Journal Article
    ​ ​Hofner B, Hothorn T, Kneib T, Schmid M. ​A Framework for Unbiased Model Selection Based on Boosting​. ​​Journal of Computational and Graphical Statistics. ​2011;​20​(4):​​956​-971​. ​doi:10.1198/jcgs.2011.09220. 
    ​Details  DOI 
  • 2011 | Book Chapter
    ​ ​Fahrmeir L, Kneib T. ​Spatial Smoothing, Interactions and Geoadditive Regression​. ​​Bayesian Smoothing and Regression for Longitudinal, Spatial and Event History Data. ​2011. p. 307​-414​. ​doi:10.1093/acprof:oso/9780199533022.003.0005. 
    ​Details  DOI 
  • 2011 | Book Chapter
    ​ ​Fahrmeir L, Kneib T. ​Event History Data​. ​​Bayesian Smoothing and Regression for Longitudinal, Spatial and Event History Data. ​2011. p. 415​-494​. ​doi:10.1093/acprof:oso/9780199533022.003.0006. 
    ​Details  DOI 
  • 2011 | Book Chapter
    ​ ​Fahrmeir L, Kneib T. ​Introduction: Scope of the Book and Applications​. ​​Bayesian Smoothing and Regression for Longitudinal, Spatial and Event History Data. ​2011. p. 1​-17​. ​doi:10.1093/acprof:oso/9780199533022.003.0001. 
    ​Details  DOI 
  • 2011 | Book Chapter
    ​ ​Fahrmeir L, Kneib T. ​Generalized Linear Mixed Models​. ​​Bayesian Smoothing and Regression for Longitudinal, Spatial and Event History Data. ​2011. p. 107​-177​. ​doi:10.1093/acprof:oso/9780199533022.003.0003. 
    ​Details  DOI 
  • 2011 | Book Chapter
    ​ ​Fahrmeir L, Kneib T. ​Basic Concepts for Smoothing and Semiparametric Regression​. ​​Bayesian Smoothing and Regression for Longitudinal, Spatial and Event History Data. ​2011. p. 18​-106​. ​doi:10.1093/acprof:oso/9780199533022.003.0002. 
    ​Details  DOI 
  • 2011 | Book Chapter
    ​ ​Fahrmeir L, Kneib T. ​Semiparametric Mixed Models for Longitudinal Data​. ​​Bayesian Smoothing and Regression for Longitudinal, Spatial and Event History Data. ​2011. p. 178​-306​. ​doi:10.1093/acprof:oso/9780199533022.003.0004. 
    ​Details  DOI 
  • 2011 | Journal Article | 
    ​ ​Köpcke L, Furche J, Juárez Paz L, Kneib T, Kretzberg J. ​Comparison of a Bayesian and a regression model for stimulus classification​. ​​BMC Neuroscience. ​2011;​12​(S1). ​doi:10.1186/1471-2202-12-S1-P178. 
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  • 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. 
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  • 2010 | Report
    ​ ​Mayr A, Hofner B, Kneib T, Schmid M. ​GAMLSS for high-dimensional data – a flexible approach based on boosting​. ​​2010. 
    ​Details 
  • 2010 | Conference Paper
    ​ ​Pata MP, Kneib T, Cadarso-Suárez C, Lustres-Pérez V, Fernández-Pulpeiro E. ​Categorical structured additive regression for the assessment of habitat suitability in the spatial distribution of mussel seed abundance​. ​ Paper presented at:​ METMAV International Workshop on Spatio-Temporal Modelling; ​2010-06-30​ - 2010-07-02​; ​​Santiago de Compostela. doi:10.13140/2.1.3714.8806.
    ​Details  DOI 
  • 2010 | Journal Article
    ​ ​Cadarso-Suárez C, Meira-Machado L, Kneib T, Gude F. ​Flexible hazard ratio curves for continuous predictors in multi-state models​. ​​Statistical Modelling. ​2010;​10​(3):​​291​-314​. ​doi:10.1177/1471082X0801000303. 
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  • 2010 | Journal Article
    ​ ​Kneib T, Konrath S, Fahrmeir L. ​High dimensional structured additive regression models: Bayesian regularization, smoothing and predictive performance​. ​​Journal of the Royal Statistical Society. Series C, Applied statistics. ​2010;​60​(1):​​51​-70​. ​doi:10.1111/j.1467-9876.2010.00723.x. 
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  • 2010 | Journal Article
    ​ ​Alexopoulos P, Lehrl S, Richter-Schmidinger T, Kreusslein A, Hauenstein T, Bayerl F, et al. ​Short-term influence of elevation of plasma homocysteine levels on cognitive function in young healthy adults​. ​​The Journal of Nutrition, Health & Aging. ​2010;​14​(4):​​283​-287​. ​doi:10.1007/s12603-010-0062-5. 
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  • 2010 | Journal Article
    ​ ​Alexopoulos P, Ebert A, Richter-Schmidinger T, Schöll E, Natale B, Aguilar CA, et al. ​Validation of the German Revised Addenbrooke’s Cognitive Examination for Detecting Mild Cognitive Impairment, Mild Dementia in Alzheimer’s Disease and Frontotemporal Lobar Degeneration​. ​​Dementia and Geriatric Cognitive Disorders. ​2010;​29​(5):​​448​-456​. ​doi:10.1159/000312685. 
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  • 2010 | Journal Article
    ​ ​Cadarso-Suarez C, Meira-Machado L, Kneib T, Gude F. ​Flexible hazard ratio curves for continuous predictors in multi-state models: an application to breast cancer data​. ​​Statistical Modelling. ​2010;​10​(3):​​291​-314​. ​doi:10.1177/1471082x0801000303. 
    ​Details  DOI 
  • 2010 | Book Chapter
    ​ ​Kneib T. ​Semiparametric Regression​. ​In: Leidl R, Hartmann AK​, editors. ​Modern Comutational Science 2010. ​Oldenburg University Press; ​2010. 
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  • 2010 | Journal Article
    ​ ​Hothorn T, Müller J, Schröder B, Kneib T, Brandl R. ​Decomposing environmental, spatial, and spatiotemporal components of species distributions​. ​​Ecological Monographs. ​2010;​81​(2):​​329​-347​. ​doi:10.1890/10-0602.1. 
    ​Details  DOI 
  • 2010 | Journal Article
    ​ ​Wiesenfarth M, Kneib T. ​Bayesian geoadditive sample selection models​. ​​Journal of the Royal Statistical Society. Series C, Applied statistics. ​2010;​59​(3):​​381​-404​. ​doi:10.1111/j.1467-9876.2009.00698.x. 
    ​Details  DOI 
  • 2010 | Book Chapter
    ​ ​Kneib T, Brezger A, Crainiceanu CM. ​Generalized Semiparametric Regression Models with Nonparametric Effects of Covariates Measured with Error​. ​In: Kneib T, Tutz G​, editors. ​Statistical Modelling and Regression Structures - Festschrift in the Honour of Ludwig Fahrmeir. ​Berlin: ​Springer; ​2010. 
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  • 2010 | Book Chapter
    ​ ​Kneib T. ​Exploratory Data Analysis​. ​In: Leidl R, Hartmann AK​, editors. ​Modern Comutational Science 2010. ​Oldenburg University Press; ​2010. 
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  • 2010 | Journal Article
    ​ ​Richter-Schmidinger T, Alexopoulos P, Horn M, Maus S, Reichel M, Rhein C, et al. ​Influence of brain-derived neurotrophic-factor and apolipoprotein E genetic variants on hippocampal volume and memory performance in healthy young adults​. ​​Journal of Neural Transmission. ​2010;​118​(2):​​249​-257​. ​doi:10.1007/s00702-010-0539-8. 
    ​Details  DOI 
  • 2010 | Journal Article
    ​ ​Greven S, Kneib T. ​On the behaviour of marginal and conditional AIC in linear mixed models​. ​​Biometrika. ​2010;​97​(4):​​773​-789​. ​doi:10.1093/biomet/asq042. 
    ​Details  DOI 
  • 2010 | Book Chapter
    ​ ​Kneib T, Brezger A, Crainiceanu CM. ​Generalized Semiparametric Regression with Covariates Measured with Error​. ​​Statistical modelling and regression structures. ​2010. p. 133​-154​. ​doi:10.1007/978-3-7908-2413-1_8. 
    ​Details  DOI 
  • 2010 | Journal Article
    ​ ​Krivobokova T, Kneib T, Claeskens G. ​Simultaneous Confidence Bands for Penalized Spline Estimators​. ​​Journal of the American Statistical Association. ​2010;​105​(490):​​852​-863​. ​doi:10.1198/jasa.2010.tm09165. 
    ​Details  DOI 
  • 2010 | Journal Article
    ​ ​Sobotka F, Kneib T. ​Geoadditive expectile regression​. ​​Computational Statistics & Data Analysis. ​2010;​56​(4):​​755​-767​. ​doi:10.1016/j.csda.2010.11.015. 
    ​Details  DOI 
  • 2010 | Anthology
    ​ ​Kneib T, Tutz G​, editors. ​Statistical Modelling and Regression Structures: ​Festschrift in Honour of Ludwig Fahrmeir​. ​​Heidelberg: ​Physica-Verlag HD; ​2010. ​XXIV, 472 p. doi:10.1007/978-3-7908-2413-1. 
    ​Details  DOI 
  • 2010 | Journal Article | 
    ​ ​Hothorn T, Bühlmann P, Kneib T, Schmid M, Hofner B. ​Model-based Boosting 2.0​. ​​Journal of Machine Learning Reseach - Machine Learning Open Source Software. ​2010;​11​:​​2109​-2113​. ​
    ​Details 
  • 2009 | Preprint
    ​ ​Kneib T, Greven S. ​On the Behavior of Marginal and Conditional Akaike Information Criteria in Linear Mixed Models​. ​​2009. 
    ​Details 
  • 2009 | Preprint
    ​ ​Kneib T. ​Mathematische Grundlagen der Angewandten Statistik​. ​​2009. 
    ​Details 
  • 2009 | Journal Article
    ​ ​Kneib T, Knauer F, Küchenhoff H. ​A general approach to the analysis of habitat selection​. ​​Environmental and Ecological Statistics. ​2009;​18​(1):​​1​-25​. ​doi:10.1007/s10651-009-0115-2. 
    ​Details  DOI 
  • 2009 | Book Chapter
    ​ ​Fahrmeir L, Kneib T, Lang S. ​Nichtparametrische Regression​. ​​Regression: Modelle, Methoden und Anwendungen. ​2. ed. ​Berlin: ​Springer; ​2009. p. 291​-398​. ​doi:10.1007/978-3-642-01837-4_7. 
    ​Details  DOI 
  • 2009 | Journal Article
    ​ ​Scheipl F, Kneib T. ​Locally adaptive Bayesian P-splines with a Normal-Exponential-Gamma prior​. ​​Computational Statistics & Data Analysis. ​2009;​53​(10):​​3533​-3552​. ​doi:10.1016/j.csda.2009.03.009. 
    ​Details  DOI 
  • 2009 | Book Chapter
    ​ ​Fahrmeir L, Kneib T, Lang S. ​Regressionsmodelle​. ​​Regression: Modelle, Methoden und Anwendungen. ​2. ed. ​Berlin: ​Springer; ​2009. p. 19​-58​. ​doi:10.1007/978-3-642-01837-4_2. 
    ​Details  DOI 
  • 2009 | Journal Article
    ​ ​Beyersmann J, Kneib T, Schumacher M, Gastmeier P. ​Nosocomial Infection, Length of Stay, and Time-Dependent Bias​. ​​Infection Control & Hospital Epidemiology. ​2009;​30​(3):​​273​-276​. ​doi:10.1086/596020. 
    ​Details  DOI 
  • 2009 | Book Chapter
    ​ ​Fahrmeir L, Kneib T, Lang S. ​Strukturiert-additive Regression​. ​​Regression: Modelle, Methoden und Anwendungen. ​2. ed. ​Berlin: ​Springer; ​2009. p. 399​-443​. ​doi:10.1007/978-3-642-01837-4_8. 
    ​Details  DOI 
  • 2009 | Book Chapter
    ​ ​Fahrmeir L, Kneib T, Lang S. ​Kategoriale Regressionsmodelle​. ​​Regression: Modelle, Methoden und Anwendungen. ​2. ed. ​Berlin: ​Springer; ​2009. p. 235​-252​. ​doi:10.1007/978-3-642-01837-4_5. 
    ​Details  DOI 
  • 2009 | Monograph
    ​ ​Fahrmeir L, Kneib T, Lang S. ​Regression: ​Modelle, Methoden und Anwendungen​. ​​2. ed. ​Berlin: ​Springer; ​2009. ​XVI, 501 p. doi:10.1007/978-3-642-01837-4. 
    ​Details  DOI 
  • 2009 | Book Chapter
    ​ ​Fahrmeir L, Kneib T, Lang S. ​Generalisierte lineare Modelle​. ​​Regression: Modelle, Methoden und Anwendungen. ​2. ed. ​Berlin: ​Springer; ​2009. p. 189​-234​. ​doi:10.1007/978-3-642-01837-4_4. 
    ​Details  DOI 
  • 2009 | Book Chapter
    ​ ​Fahrmeir L, Kneib T, Lang S. ​Lineare Regressionsmodelle​. ​​Regression: Modelle, Methoden und Anwendungen. ​2. ed. ​Berlin: ​Springer; ​2009. p. 59​-188​. ​doi:10.1007/978-3-642-01837-4_3. 
    ​Details  DOI 
  • 2009 | Journal Article
    ​ ​Alexopoulos P, Günther F, Popp J, Jessen F, Peters O, Wolf S, et al. ​PLASMA HOMOCYSTEINE AND CEREBROSPINAL FLUID NEURODEGENERATION BIOMARKERS IN MILD COGNITIVE IMPAIRMENT AND DEMENTIA​. ​​Journal of the American Geriatrics Society. ​2009;​57​(4):​​737​-739​. ​doi:10.1111/j.1532-5415.2009.02212.x. 
    ​Details  DOI 
  • 2009 | Preprint
    ​ ​Kneib T. ​Additive Quantile Regression for the Analysis of Childhood Malnutrition​. ​​2009. 
    ​Details 
  • 2009 | Lecture
    ​ ​Kneib T. ​Statistical Modelling Based on Structured Additive Regression​. ​​2009-01-16; 
    ​Details 
  • 2009 | Journal Article
    ​ ​Bässler C, Müller J, Hothorn T, Kneib T, Badeck F, Dziock F. ​Estimation of the extinction risk for high-montane species as a consequence of global warming and assessment of their suitability as cross-taxon indicators​. ​​Ecological Indicators. ​2009;​10​(2):​​341​-352​. ​doi:10.1016/j.ecolind.2009.06.014. 
    ​Details  DOI 
  • 2009 | Preprint
    ​ ​Kneib T. ​BayesX and INLA - Opponents or Partners?​. ​​2009. 
    ​Details 
  • 2009 | Journal Article
    ​ ​Fahrmeir L, Kneib T, Konrath S. ​Bayesian regularisation in structured additive regression: a unifying perspective on shrinkage, smoothing and predictor selection​. ​​Statistics and Computing. ​2009;​20​(2):​​203​-219​. ​doi:10.1007/s11222-009-9158-3. 
    ​Details  DOI 
  • 2009 | Journal Article
    ​ ​Fahrmeir L, Kneib T. ​Discussion on "Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations" by Rue, H., Martino, S. and Chopin, N​. ​​Journal of the Royal Statistical Society B: STATISTICAL METHODOLOGY. ​2009;​71​(2). ​
    ​Details 
  • 2009 | Book Chapter
    ​ ​Fahrmeir L, Kneib T, Lang S. ​Gemischte Modelle​. ​In: Fahrmeir L, Kneib T, Lang S​, editors. ​Regression: Modelle, Methoden und Anwendungen. ​Berlin, Heidelberg: ​Springer; ​2009. p. 253​-290​. ​doi:10.1007/978-3-642-01837-4_6. 
    ​Details  DOI 
  • 2009 | Book Chapter
    ​ ​Kneib T. ​Exploratory Data Analysis​. ​In: Leidl R, Hartmann AK​, editors. ​Modern Comutational Science 2009. ​Oldenburg University Press; ​2009. 
    ​Details 
  • 2009 | Journal Article
    ​ ​Kalus S, Kneib T, Steiger A, Holsboer F, Yassouridis A. ​A new strategy to analyze possible association structures between dynamic nocturnal hormone activities and sleep alterations in humans​. ​​AJP: Regulatory, Integrative and Comparative Physiology. ​2009;​296​(4):​​R1216​-R1227​. ​doi:10.1152/ajpregu.90530.2008. 
    ​Details  DOI  PMID  PMC 
  • 2008 | Journal Article
    ​ ​Moubarak P, Zilker S, Wolf H, Hofner B, Kneib T, Küchenhoff H, et al. ​Activity-guided antithrombin III therapy in severe surgical sepsis: efficacy and safety according to a retrospective data analysis​. ​​Shock. ​2008;​30​(6):​​634​-641​. ​doi:10.1097/SHK.0b013e31817d3e14. 
    ​Details  DOI  PMID  PMC 
  • 2008 | Report
    ​ ​Konrath S, Kneib T, Fahrmeir L. ​Bayesian Regularisation in Structured Additive Regression Models for Survival Data​. ​​2008. doi: https://doi.org/10.5282/ubm/epub.5732 
    ​Details  DOI 
  • 2008 | Preprint
    ​ ​Hofner B, Hothorn T, Kneib T. ​CoxFlexBoost: Fitting Structured Survival Models​. ​​2008. 
    ​Details 
  • 2008 | Journal Article
    ​ ​Kneib T, Hennerfeind A. ​Bayesian semi parametric multi-state models​. ​​Statistical Modelling. ​2008;​8​(2):​​169​-198​. ​doi:10.1177/1471082x0800800203. 
    ​Details  DOI 
  • 2008 | Book Chapter
    ​ ​Fahrmeir L, Kneib T. ​On the Identification of Trend and Correlation in Temporal and Spatial Regression​. ​In: Heumann C​, editor. ​Recent Advances in Linear Models and Related Areas: Essays in Honour of Helge Toutenburg. ​Heidelberg: ​Physica-Verlag HD; ​2008. p. 1​-27​. ​doi:10.1007/978-3-7908-2064-5_1. 
    ​Details  DOI 
  • 2008 | Preprint
    ​ ​Kneib T. ​Semiparametrische Multinomiale Logit-Modelle zur Markenwahl-Analyse​. ​​2008. 
    ​Details 
  • 2008 | Preprint
    ​ ​Kneib T. ​Suchergebnisse Webergebnisse Zeitvariierende Effekte in Markenwahl-Modellen​. ​​2008. 
    ​Details 
  • 2008 | Preprint
    ​ ​Kneib T. ​Time-varying Coe-cients in Brand Choice Modelling​. ​​2008. 
    ​Details 
  • 2008 | Journal Article
    ​ ​Müller J, Bußler H, Kneib T. ​Saproxylic beetle assemblages related to silvicultural management intensity and stand structures in a beech forest in Southern Germany​. ​​Journal of Insect Conservation. ​2008;​12​(2):​​107​-124​. ​doi:10.1007/s10841-006-9065-2. 
    ​Details  DOI 
  • 2008 | Preprint
    ​ ​Kneib T. ​Bayesianische Regularisierung in semiparametrischen Regressionsmodellen​. ​​2008. 
    ​Details 
  • 2008 | Journal Article
    ​ ​Moubarak P, Zilker S, Wolf H, Hofner B, Kneib T, Küchenhoff H, et al. ​ACTIVITY-GUIDED ANTITHROMBIN III THERAPY IN SEVERE SURGICAL SEPSIS​. ​​Shock. ​2008;​30​(6):​​634​-641​. ​doi:10.1097/shk.0b013e31817d3e14. 
    ​Details  DOI 
  • 2008 | Preprint
    ​ ​Kneib T, Hothorn T, Trutz G. ​Boosting Geoadditive Regression Models​. ​​2008. 
    ​Details 
  • 2008 | Journal Article
    ​ ​Fahrmeir L, Kneib T. ​Propriety of posteriors in structured additive regression models: Theory and empirical evidence​. ​​Journal of Statistical Planning and Inference. ​2008;​139​(3):​​843​-859​. ​doi:10.1016/j.jspi.2008.05.036. 
    ​Details  DOI 
  • 2008 | Preprint
    ​ ​Kneib T, Hothorn T. ​mboost - Componentwise Boosting for Generalised Regression Models​. ​​2008. 
    ​Details 
  • 2008 | Journal Article
    ​ ​Kneib T, Hothorn T, Tutz G. ​Variable Selection and Model Choice in Geoadditive Regression Models​. ​​Biometrics. ​2008;​65​(2):​​626​-634​. ​doi:10.1111/j.1541-0420.2008.01112.x. 
    ​Details  DOI 
  • 2008 | Journal Article
    ​ ​Kneib T, Müller J, Hothorn T. ​Spatial smoothing techniques for the assessment of habitat suitability​. ​​Environmental and Ecological Statistics. ​2008;​15​(3):​​343​-364​. ​doi:10.1007/s10651-008-0092-x. 
    ​Details  DOI 
  • 2008 | Journal Article
    ​ ​Strasak AM, Lang S, Kneib T, Brant LJ, Klenk J, Hilbe W, et al. ​Use of Penalized Splines in Extended Cox-Type Additive Hazard Regression to Flexibly Estimate the Effect of Time-varying Serum Uric Acid on Risk of Cancer Incidence: A Prospective, Population-Based Study in 78,850 Men​. ​​Annals of Epidemiology. ​2008;​19​(1):​​15​-24​. ​doi:10.1016/j.annepidem.2008.08.009. 
    ​Details  DOI 
  • 2008 | Journal Article | 
    ​ ​Strobl C, Boulesteix, Anne-Laure, Kneib T, Augustin T, Zeileis A. ​Conditional Variable Importance for Random Forests​. ​​BMC Bioinformatics. ​2008;​9​(1):​​1​-11​. ​doi:10.1186/1471-2105-9-307. 
    ​Details  DOI 
  • 2008 | Journal Article
    ​ ​Weidinger S, Baurecht H, Wagenpfeil S, Henderson J, Novak N, Sandilands A, et al. ​Analysis of the individual and aggregate genetic contributions of previously identified serine peptidase inhibitor Kazal type 5 (SPINK5), kallikrein-related peptidase 7 (KLK7), and filaggrin (FLG) polymorphisms to eczema risk​. ​​Journal of Allergy and Clinical Immunology. ​2008;​122​(3):​​560​-568​. ​doi:10.1016/j.jaci.2008.05.050. 
    ​Details  DOI  PMID  PMC 
  • 2007 | Report
    ​ ​Kneib T, Belitz C, Brezger A, Lang S. ​BayesX - Bayesian inference in structured additive regression​. ​​2007. 
    ​Details 
  • 2007 | Monograph
    ​ ​Fahrmeir L, Kneib T, Lang S. ​Regression: ​Modelle, Methoden und Anwendungen ; mit 51 Tabellen​. ​​Berlin: ​Springer; ​2007. (Statistik und ihre Anwendungen). doi:10.1007/978-3-540-33933-5. 
    ​Details  DOI 
  • 2007 | Book Chapter
    ​ ​Fahrmeir L, Kneib T, Lang S. ​Kategoriale Regressionsmodelle​. ​​Regression: Modelle, Methoden und Anwendungen. ​Berlin: ​Springer; ​2007. p. 235​-252​. ​doi:10.1007/978-3-540-33933-5_5. 
    ​Details  DOI 
  • 2007 | Book Chapter
    ​ ​Fahrmeir L, Kneib T, Lang S. ​Strukturiert-additive Regression​. ​​Regression: Modelle, Methoden und Anwendungen. ​Berlin: ​Springer; ​2007. p. 399​-443​. ​doi:10.1007/978-3-540-33933-5_8. 
    ​Details  DOI 
  • 2007 | Book Chapter
    ​ ​Fahrmeir L, Kneib T, Lang S. ​Regressionsmodelle​. ​​Regression: Modelle, Methoden und Anwendungen. ​Berlin: ​Springer; ​2007. p. 19​-58​. ​doi:10.1007/978-3-540-33933-5_2. 
    ​Details  DOI 
  • 2007 | Book Chapter
    ​ ​Fahrmeir L, Kneib T, Lang S. ​Generalisierte lineare Modelle​. ​​Regression: Modelle, Methoden und Anwendungen. ​Berlin: ​Springer; ​2007. p. 189​-234​. ​doi:10.1007/978-3-540-33933-5_4. 
    ​Details  DOI 
  • 2007 | Book Chapter
    ​ ​Fahrmeir L, Kneib T, Lang S. ​Nichtparametrische Regression​. ​​Regression: Modelle, Methoden und Anwendungen. ​Berlin: ​Springer; ​2007. p. 291​-398​. ​doi:10.1007/978-3-540-33933-5_7. 
    ​Details  DOI 
  • 2007 | Book Chapter
    ​ ​Fahrmeir L, Kneib T, Lang S. ​Lineare Regressionsmodelle​. ​​Regression: Modelle, Methoden und Anwendungen. ​Berlin: ​Springer; ​2007. p. 59​-188​. ​doi:10.1007/978-3-540-33933-5_3. 
    ​Details  DOI 
  • 2007 | Preprint
    ​ ​Kneib T, Hennerfeind A. ​Semiparametric Event History Models for Analyzing Human Sleep Data​. ​​2007. 
    ​Details 
  • 2007 | Journal Article
    ​ ​Kneib T, Baumgartner B, Steiner WJ. ​Semiparametric multinomial logit models for analysing consumer choice behaviour​. ​​AStA Advances in Statistical Analysis. ​2007;​91​(3):​​225​-244​. ​doi:10.1007/s10182-007-0033-2. 
    ​Details  DOI 
  • 2007 | Journal Article
    ​ ​Kneib T, Petzoldt T. ​Introduction to the Special Volume on "Ecology and Ecological Modelling in R"​. ​​Journal of Statistical Software. ​2007;​22​(1). ​doi:10.18637/jss.v022.i01. 
    ​Details  DOI 
  • 2006 | Thesis | Bachelor Thesis
    ​ ​Kneib T. ​Mixed model based inference in structured additive regression​ [dissertation]. ​​Dr. Hut-Verlag; ​2006. 
    ​Details 
  • 2006 | Journal Article
    ​ ​Kneib T. ​Mixed model-based inference in geoadditive hazard regression for interval-censored survival times​. ​​Computational Statistics & Data Analysis. ​2006;​51​(2):​​777​-792​. ​doi:10.1016/j.csda.2006.06.019. 
    ​Details  DOI 
  • 2006 | Preprint
    ​ ​Kneib T. ​Empirical Bayes Inference in Structured Hazard Regression​. ​​2006. 
    ​Details 
  • 2006 | Preprint
    ​ ​Hennerfeind A, Fahrmeir L, Kneib T. ​Bayesian Structured Hazard Regression​. ​​2006. 
    ​Details 
  • 2006 | Preprint
    ​ ​Kneib T. ​BayesX: Bayesianische Inferenz in strukturiert additiven Regressionsmodellen​. ​​2006. 
    ​Details 
  • 2006 | Journal Article
    ​ ​Kneib T, Fahrmeir L. ​Structured Additive Regression for Categorical Space-Time Data: A Mixed Model Approach​. ​​Biometrics. ​2006;​62​(1):​​109​-118​. ​doi:10.1111/j.1541-0420.2005.00392.x. 
    ​Details  DOI 
  • 2006 | Journal Article
    ​ ​Kneib T, Fahrmeir L. ​A Mixed Model Approach for Geoadditive Hazard Regression​. ​​Scandinavian Journal of Statistics. ​2006;​34​(1):​​207​-228​. ​doi:10.1111/j.1467-9469.2006.00524.x. 
    ​Details  DOI 
  • 2005 | Preprint
    ​ ​Kneib T, Fahrmeir L. ​Supplement to "Structured additive regression for categorical space-time data: A mixed model approach"​. ​​2005. 
    ​Details 
  • 2005 | Preprint
    ​ ​Kneib T. ​Analysing geoadditive regression data: a mixed model approach​. ​​2005. 
    ​Details 
  • 2005 | Preprint
    ​ ​Kneib T, Fahrmeir L. ​Gemischte Modelle zur Schatzung geoadditiver Regressionsmodelle​. ​​2005. 
    ​Details 
  • 2004 | Preprint
    ​ ​Kneib T, Fahrmeir L. ​A mixed model approach for structured hazard regression​. ​​2004. Available from: https://doi.org/10.5282/ubm/epub.1770​
    ​Details  DOI 
  • 2004 | Preprint
    ​ ​Kneib T, Fahrmeir L. ​Structured additive regression for multicategorical space-time data: A mixed model approach​. ​​2004. Available from: https://doi.org/10.5282/ubm/epub.1748​
    ​Details  DOI 
  • 2004 | Report
    ​ ​Kneib T, Lang S, Brezger A. ​Bayesian semiparametric regression based on mixed model methodology: A tutorial​. ​​Department of Statistics, University of Munich; ​2004. 
    ​Details 
  • 2004 | Journal Article
    ​ ​Fahrmeir L, Kneib T, Lang S. ​Penalized structured additive regression for space-time data: a Bayesian perspective​. ​​Statistica Sinica. ​2004;​14​:​​731​-761​. ​
    ​Details 
  • 2004 | Journal Article
    ​ ​Kneib T. ​Penalized structured additive regression for space-time data​. ​​Biometrical Journal. ​2004;​46​(S1):​​1​-1​. ​doi:10.1002/bimj.200490310. 
    ​Details  DOI 
  • 2004 | Working Paper
    ​ ​Kneib T, Lang S, Brezger A. ​Bayesian semiparametric regression based on MCMC techniques: A tutorial​. ​​Department of Statistics, University of Munich; ​2004. 
    ​Details 
  • 2004 | Preprint
    ​ ​Kneib T. ​Modelling geoadditive survival data​. ​​2004. 
    ​Details 
  • 2004 | Preprint
    ​ ​Kneib T, Fahrmeir L, Lang S. ​A general mixed model approach for spatio-temporal regression data​. ​​2004. 
    ​Details 
  • 2004 | Preprint
    ​ ​Kneib T. ​Penalized structured additive regression for multicategorical space-time data​. ​​2004. 
    ​Details 
  • 2003 | Report
    ​ ​Kneib T, Bretzger A, Lang S. ​BayesX Manuals: ​Reference Manual, Methodology Manual, Tutorials Manual​. ​​2003. 
    ​Details 
  • 2003 | Thesis | Diploma Thesis
    ​ ​Kneib T. ​Bayes-Inferenz in generalisierten geoadditiven gemischten Modellen​ [dissertation]. ​[München]: ​Ludwig-Maximilians-Universität München, Institut für Statistik; ​2003. ​235 p. 
    ​Details 
  • 2003 | Report
    ​ ​Kneib T. ​Restricted maximum likelihood estimation of variance parameters in generalized linear mixed models​. ​​2003. 
    ​Details 

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