Prof. Dr. Thomas Kneib

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  • 2023 | Preprint
    ​ ​Rappl, A., Kneib, T., Lang, S. & Bergherr, E. (2023). "Spatial Joint Models through Bayesian Structured Piece-wise Additive Joint Modelling for Longitudinal and Time-to-Event Data".​ Unpublished manuscript. ​
    ​Details 
  • 2022 | Journal Article | Research Paper | 
    ​ ​Martins, R., Sousa, B. d., Kneib, T., Hohberg, M., Klein, N., Duarte, E. & Rodrigues, V. (2022). ​Is age at menopause decreasing? – The consequences of not completing the generational cohort. BMC Medical Research Methodology, 22(1), Article 187​. ​doi: https://doi.org/10.1186/s12874-022-01658-x 
    ​Details  DOI 
  • 2022 | Journal Article
    ​ ​Wiemann, P. F., Klein, N. & Kneib, T. (2022). ​Correcting for sample selection bias in Bayesian distributional regression models. Computational Statistics & Data Analysis, 168, Article S0167947321002164​. ​doi: https://doi.org/10.1016/j.csda.2021.107382 
    ​Details  DOI 
  • 2022 | Journal Article | 
    ​ ​Seufert, J. D., Python, A., Weisser, C., Cisneros, E., Kis-Katos, K. & Kneib, T. (2022). ​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), Article rssa.12866​. ​doi: https://doi.org/10.1111/rssa.12866 
    ​Details  DOI 
  • 2022 | Journal Article | 
    ​ ​Marmolejo-Ramos, F., Barrera-Causil, C., Kuang, S., Fazlali, Z., Wegener, D., Kneib, T., De Bastiani, F. ... Martinez-Flórez, G. (2022). ​Generalised exponential-Gaussian distribution: a method for neural reaction time analysis. Cognitive Neurodynamics, . ​doi: https://doi.org/10.1007/s11571-022-09813-2 
    ​Details  DOI 
  • 2022 | Journal Article | Research Paper | 
    ​ ​Marques, I., Kneib, T. & Klein, N. (2022). ​Mitigating spatial confounding by explicitly correlating Gaussian random fields. Environmetrics, 33(5), . ​doi: https://doi.org/10.1002/env.2727 
    ​Details  DOI 
  • 2022 | Journal Article | 
    ​ ​Weisser, C., Gerloff, C., Thielmann, A., Python, A., Reuter, A., Kneib, T. & Säfken, B. (2022). ​Pseudo-document simulation for comparing LDA, GSDMM and GPM topic models on short and sparse text using Twitter data. Computational Statistics, . ​doi: https://doi.org/10.1007/s00180-022-01246-z 
    ​Details  DOI 
  • 2022 | Journal Article | 
    ​ ​Marques, I., Kneib, T. & Klein, N. (2022). ​A non-stationary model for spatially dependent circular response data based on wrapped Gaussian processes. Statistics and Computing, 32(5), . ​doi: https://doi.org/10.1007/s11222-022-10136-9 
    ​Details  DOI 
  • 2022 | Journal Article | 
    ​ ​Carlan, M. & Kneib, T. (2022). ​Bayesian discrete conditional transformation models. Statistical Modelling, Article 1471082X2211141​. ​doi: https://doi.org/10.1177/1471082X221114177 
    ​Details  DOI 
  • 2022 | Journal Article | 
    ​ ​Marmolejo‐Ramos, F., Tejo, M., Brabec, M., Kuzilek, J., Joksimovic, S., Kovanovic, V., González, J. ... Ospina, R. (2022). ​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, . ​doi: https://doi.org/10.1002/widm.1479 
    ​Details  DOI 
  • 2022 | Journal Article
    ​ ​Melis, G. G. & Kneib, T. (2022). ​In memory of Carmen María Cadarso Suárez (1960–2022). Biometrical Journal, 64(7), ​1159​-1160​. ​doi: https://doi.org/10.1002/bimj.202270075 
    ​Details  DOI 
  • 2021 | Journal Article | Research Paper | 
    ​ ​Lasser, J., Manik, D., Silbersdorff, A., Säfken, B. & Kneib, T. (2021). ​Introductory data science across disciplines, using Python, case studies, and industry consulting projects. Teaching Statistics, 43, ​S190​-S200​. ​doi: https://doi.org/10.1111/test.12243 
    ​Details  DOI 
  • 2021 | Journal Article
    ​ ​Hambuckers, J. & Kneib, T. (2021). ​Smooth-Transition Regression Models for Non-Stationary Extremes. Journal of Financial Econometrics, . ​doi: https://doi.org/10.1093/jjfinec/nbab005 
    ​Details  DOI 
  • 2021 | Journal Article
    ​ ​Säfken, B., Rügamer, D., Kneib, T. & Greven, S. (2021). ​Conditional Model Selection in Mixed-Effects Models with cAIC4. Journal of Statistical Software, 99(8), . ​doi: https://doi.org/10.18637/jss.v099.i08 
    ​Details  DOI 
  • 2021 | Journal Article
    ​ ​Spiegel, E., Kneib, T., von Gablenz, P. & Otto‐Sobotka, F. (2021). ​Generalized expectile regression with flexible response function. Biometrical Journal, . ​doi: https://doi.org/10.1002/bimj.202000203 
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  • 2021 | Journal Article
    ​ ​Seiler, J., Harttgen, K., Kneib, T. & Lang, S. (2021). ​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, 40, ​100950​. ​doi: https://doi.org/10.1016/j.ehb.2020.100950 
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  • 2021 | Journal Article
    ​ ​Klein, N., Carlan, M., Kneib, T., Lang, S. & Wagner, H. (2021). ​Bayesian Effect Selection in Structured Additive Distributional Regression Models. Bayesian Analysis, 16(2), . ​doi: https://doi.org/10.1214/20-BA1214 
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  • 2021 | Journal Article | Research Paper
    ​ ​Seidel, D., Annighöfer, P., Thielman, A., Seifert, Q. E., Thauer, Jan-Henrik, Glatthorn, J., Ehbrecht, M. ... Ammer, C. (2021). ​Predicting Tree Species From 3D Laser Scanning Point Clouds Using Deep Learning. Frontiers in Plant Science, 12, . ​doi: https://doi.org/10.3389/fpls.2021.635440 
    ​Details  DOI 
  • 2021 | Journal Article | Research Paper | 
    ​ ​Udy, K., Fritsch, M., Meyer, K. M., Grass, I., Hanß, S., Hartig, F., Kneib, T. ... van Waveren, C. (2021). ​Environmental heterogeneity predicts global species richness patterns better than area. Global Ecology and Biogeography, 30(4), ​842​-851​. ​doi: https://doi.org/10.1111/geb.13261 
    ​Details  DOI 
  • 2021 | Journal Article | 
    ​ ​Stadlmann, S. & Kneib, T. (2021). ​Interactively visualizing distributional regression models with distreg.vis. Statistical Modelling, 22(6), ​527​-545​. ​doi: https://doi.org/10.1177/1471082X211007308 
    ​Details  DOI 
  • 2021 | Journal Article | Research Paper | 
    ​ ​Hohberg, M., Donat, F., Marra, G. & Kneib, T. (2021). ​Beyond unidimensional poverty analysis using distributional copula models for mixed ordered‐continuous outcomes. Journal of the Royal Statistical Society: Series C (Applied Statistics), 70(5), ​1365​-1390​. ​doi: https://doi.org/10.1111/rssc.12517 
    ​Details  DOI 
  • 2020 | Journal Article | 
    ​ ​Kneib, T., Otto-Sobotka, F. & Spiegel, E. (2020). ​Spatio-temporal expectile regression models. Statistical Modelling, 20(4), Article 1471082X1982994​. ​doi: https://doi.org/10.1177/1471082X19829945 
    ​Details  DOI 
  • 2020 | Journal Article
    ​ ​Michaelis, P., Klein, N. & Kneib, T. (2020). ​Mixed Discrete‐Continuous Regression – A Novel Approach Based on Weight Functions. Stat, . ​doi: https://doi.org/10.1002/sta4.277 
    ​Details  DOI 
  • 2020 | Preprint
    ​ ​Seebaß, J. V., Schlüter, J., Wacker, B. & Kneib, T. (2020). Application of an additive structured copula regression on the joint wind speed and wind direction distribution.​ Unpublished manuscript. ​
    ​Details 
  • 2020 | Preprint
    ​ ​Wacker, B., Kneib, T. & Schlüter, J. (2020). On Existence and Uniqueness of Maximum Log-Likelihood Parameter Estimation for Two-Parameter Weibull Distributions.​ Unpublished manuscript. ​
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  • 2020 | Report
    ​ ​Hambuckers, J. & Kneib, T. (2020). Smooth Transition Regression Models for Non-Stationary Extremes​​. ​doi: https://doi.org/10.2139/ssrn.3541718 
    ​Details  DOI 
  • 2020 | Journal Article
    ​ ​Klein, N., Herwartz, H. & Kneib, T. (2020). ​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, 214(2), ​513​-539​. ​doi: https://doi.org/10.1016/j.jeconom.2019.07.003 
    ​Details  DOI 
  • 2020 | Journal Article
    ​ ​Klein, N., Hothorn, T., Barbanti, L. & Kneib, T. (2020). ​Multivariate conditional transformation models. Scandinavian Journal of Statistics, . ​doi: https://doi.org/10.1111/sjos.12501 
    ​Details  DOI 
  • 2020 | Book Chapter
    ​ ​Klein, N., Kneib, T., Marra, G. & Radice, R. (2020). ​Bayesian mixed binary-continuous copula regression with an application to childhood undernutrition. InDortet-Bernadet, Jean-Luc, Y. Fan, D. Nott, M. S. Smith​ (Eds.), Flexible Bayesian Regression Modelling ​(pp. 121​-152​). ​​Elsevier. doi: https://doi.org/10.1016/B978-0-12-815862-3.00011-1 
    ​Details  DOI 
  • 2020 | Journal Article
    ​ ​Santos, B. & Kneib, T. (2020). ​Noncrossing structured additive multiple-output Bayesian quantile regression models. Statistics and Computing, . ​doi: https://doi.org/10.1007/s11222-020-09925-x 
    ​Details  DOI 
  • 2020 | Conference Paper
    ​ ​Roeder, J., Muntermann, J. & Kneib, T. (2020). ​Towards a Taxonomy for Data Heterogeneity.​Proceedings of Internationale Tagung Wirtschaftsinformatik 2020 
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  • 2020 | Journal Article
    ​ ​Voncken, L., Kneib, T., Albers, C. J., Umlauf, N. & Timmerman, M. E. (2020). ​Bayesian Gaussian distributional regression models for more efficient norm estimation. British Journal of Mathematical and Statistical Psychology, 74(1), ​99​-117​. ​doi: https://doi.org/10.1111/bmsp.12206 
    ​Details  DOI 
  • 2020 | Journal Article | Research Paper
    ​ ​Marques, I., Klein, N. & Kneib, T. (2020). ​Non-stationary spatial regression for modelling monthly precipitation in Germany. Spatial Statistics, 40, Article 100386​. ​doi: https://doi.org/10.1016/j.spasta.2019.100386 
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  • 2020 | Journal Article
    ​ ​Briseño Sanchez, G., Hohberg, M., Groll, A. & Kneib, T. (2020). ​Flexible instrumental variable distributional regression. Journal of the Royal Statistical Society: Series A (Statistics in Society), 183(4), ​1553​-1574​. ​doi: https://doi.org/10.1111/rssa.12598 
    ​Details  DOI 
  • 2020 | Journal Article | 
    ​ ​Kneib, T. (2020). ​Comments on: Inference and computation with Generalized Additive Models and their extensions. TEST, 29(2), ​351​-353​. ​doi: https://doi.org/10.1007/s11749-020-00713-3 
    ​Details  DOI 
  • 2020 | Journal Article | 
    ​ ​van der Wurp, H., Groll, A., Kneib, T., Marra, G. & Radice, R. (2020). ​Generalised joint regression for count data: a penalty extension for competitive settings. Statistics and Computing, 30(5), ​1419​-1432​. ​doi: https://doi.org/10.1007/s11222-020-09953-7 
    ​Details  DOI 
  • 2020 | Journal Article | Editorial Contribution (Editorial, Introduction, Epilogue) | 
    ​ ​Kauermann, G., Kneib, T. & Okhrin, Y. (2020). ​Editorial. Advances in Statistical Analysis, 104(1), ​1​-3​. ​doi: https://doi.org/10.1007/s10182-020-00361-w 
    ​Details  DOI 
  • 2020 | Journal Article | 
    ​ ​Hohberg, M., Pütz, P. & Kneib, T. (2020). ​Treatment effects beyond the mean using distributional regression: Methods and guidance. PLoS One, 15(2), Article e0226514​. ​doi: https://doi.org/10.1371/journal.pone.0226514 
    ​Details  DOI  PMID  PMC 
  • 2019 | Preprint
    ​ ​Santos, B. & Kneib, T. (2019). Noncrossing structured additive multiple-output Bayesian quantile regression models.​ Unpublished manuscript. ​
    ​Details 
  • 2019 | Preprint
    ​ ​van der Wurp, H., Groll, A. H., Kneib, T. & Marra, G. (2019). Generalised Joint Regression for Count Data with a Focus on Modelling Football Matches.​ Unpublished manuscript. ​
    ​Details 
  • 2019 | Preprint
    ​ ​Hambuckers, J. & Kneib, T. (2019). Operational risk, uncertainty, and the economy: a smooth transition extreme value approach.​ Unpublished manuscript. ​
    ​Details 
  • 2019 | Journal Article
    ​ ​Klein, N., Entwistle, A., Rosenberger, A., Kneib, T. & Bickeböller, H. (2019). ​Candidate-gene association analysis for a continuous phenotype with a spike at zero using parent-offspring trios. Journal of Applied Statistics, ​1​-15​. ​doi: https://doi.org/10.1080/02664763.2019.1704226 
    ​Details  DOI 
  • 2019 | Book Chapter
    ​ ​Hohberg, M., Silbersdorff, A. & Kneib, T. (2019). ​Mehr als Durchschnittsstatistik: ​Eine kritische Einführung in Regressionsmethoden jenseits des Mittelwertes. Perspektiven einer pluralen Ökonomik ​(pp. 231​-255​). ​doi: https://doi.org/10.1007/978-3-658-16145-3_10 
    ​Details  DOI 
  • 2019 | Journal Article
    ​ ​Groll, A., Hambuckers, J., Kneib, T. & Umlauf, N. (2019). ​LASSO-type penalization in the framework of generalized additive models for location, scale and shape. Computational Statistics & Data Analysis, 140, ​59​-73​. ​doi: https://doi.org/10.1016/j.csda.2019.06.005 
    ​Details  DOI 
  • 2019 | Journal Article
    ​ ​Thaden, H., Klein, N. & Kneib, T. (2019). ​Multivariate effect priors in bivariate semiparametric recursive Gaussian models. Computational Statistics & Data Analysis, 137, ​51​-66​. ​doi: https://doi.org/10.1016/j.csda.2018.12.004 
    ​Details  DOI 
  • 2019 | Journal Article
    ​ ​Kneib, T., Klein, N., Lang, S. & Umlauf, N. (2019). ​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, 28(1), ​1​-39​. ​doi: https://doi.org/10.1007/s11749-019-00631-z 
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  • 2019 | Journal Article
    ​ ​Kneib, T., Klein, N., Lang, S. & Umlauf, N. (2019). ​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, 28(1), ​55​-59​. ​doi: https://doi.org/10.1007/s11749-019-00636-8 
    ​Details  DOI 
  • 2019 | Journal Article | Erratum
    ​ ​Greven, S. & Kneib, T. (2019). ​Correction to: On the behaviour of marginal and conditional AIC in linear mixed models. Biometrika, Article asz051​. ​doi: https://doi.org/10.1093/biomet/asz051 
    ​Details  DOI 
  • 2019 | Preprint
    ​ ​Wiemann, P. & Kneib, T. (2019). Using the Softplus Function to Construct Alternative Link Functions in Generalized Linear Models and Beyond.​ Unpublished manuscript. ​
    ​Details 
  • 2019 | Preprint
    ​ ​Hohberg, M., Donat, F., Marra, G. & Kneib, T. (2019). Beyond unidimensional poverty analysis using distributional copula models for mixed ordered-continuous outcomes.​ Unpublished manuscript. ​
    ​Details 
  • 2019 | Journal Article
    ​ ​Filippou, P., Kneib, T., Marra, G. & Radice, R. (2019). ​A trivariate additive regression model with arbitrary link functions and varying correlation matrix. Journal of Statistical Planning and Inference, 199, ​236​-248​. ​doi: https://doi.org/10.1016/j.jspi.2018.07.002 
    ​Details  DOI 
  • 2019 | Preprint
    ​ ​Gutleb, D. R., Roos, C., Heistermann, M., De Moor, D., Kneib, T., Noll, A., Schülke, O. ... Ostner, J. (2019). A multi-locus genetic risk score modulates social buffering of HPA axis activity in wild male primates.​ Unpublished manuscript. ​
    ​Details 
  • 2019 | Journal Article
    ​ ​Klein, N. & Kneib, T. (2019). ​Directional bivariate quantiles: a robust approach based on the cumulative distribution function. Advances in Statistical Analysis, . ​doi: https://doi.org/10.1007/s10182-019-00355-3 
    ​Details  DOI 
  • 2019 | Preprint
    ​ ​Stadlmann, S. & Kneib, T. (2019). distreg.vis: Interactively visualizing distributional regression models.​ Unpublished manuscript. ​
    ​Details 
  • 2019 | Journal Article
    ​ ​Pollice, A., Jona Lasinio, G., Rossi, R., Amato, M., Kneib, T. & Lang, S. (2019). ​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, 33(3), ​747​-763​. ​doi: https://doi.org/10.1007/s00477-019-01667-1 
    ​Details  DOI 
  • 2019 | Journal Article
    ​ ​Otto-Sobotka, F., Salvati, N., Ranalli, M. G. & Kneib, T. (2019). ​Adaptive semiparametric M-quantile regression. Econometrics and Statistics, 11, ​116​-129​. ​doi: https://doi.org/10.1016/j.ecosta.2019.03.001 
    ​Details  DOI 
  • 2019 | Journal Article | 
    ​ ​Signer, J., Filla, M., Schoneberg, S., Kneib, T., Bufka, L., Belotti, E. & Heurich, M. (2019). ​Rocks rock: the importance of rock formations as resting sites of the Eurasian lynx Lynx lynx. Wildlife Biology, 2019(1), . ​doi: https://doi.org/10.2981/wlb.00489 
    ​Details  DOI 
  • 2019 | Journal Article | Research Paper | 
    ​ ​Darras, K. F. A., Corre, M. D., Formaglio, G., Tjoa, A., Potapov, A., Brambach, F., Sibhatu, K. T. ... Veldkamp, E. (2019). ​Reducing Fertilizer and Avoiding Herbicides in Oil Palm Plantations - Ecological and Economic Valuations. Frontiers in Forests and Global Change, 2, . ​doi: https://doi.org/10.3389/ffgc.2019.00065 
    ​Details  DOI 
  • 2019 | Journal Article | 
    ​ ​Säfken, B. & Kneib, T. (2019). ​Conditional covariance penalties for mixed models. Scandinavian Journal of Statistics, 47(3), ​990​-1010​. ​doi: https://doi.org/10.1111/sjos.12437 
    ​Details  DOI 
  • 2019 | Journal Article
    ​ ​Espasandín-Domínguez, J., Cadarso-Suárez, C., Kneib, T., Marra, G., Klein, N., Radice, R., Lado-Baleato, O. ... Gude, F. (2019). ​Assessing the relationship between markers of glycemic control through flexible copula regression models. Statistics in Medicine, 38(27), ​5161​-5181​. ​doi: https://doi.org/10.1002/sim.8358 
    ​Details  DOI  PMID  PMC 
  • 2019 | Journal Article
    ​ ​Martini, J. W R, Rosales, F., Ha, Ngoc-Thuy, Heise, J., Wimmer, V. & Kneib, T. (2019). ​Lost in Translation: On the Problem of Data Coding in Penalized Whole Genome Regression with Interactions. G3: Genes, Genomes, Genetics, 9(4), ​1117​-1129​. ​doi: https://doi.org/10.1534/g3.118.200961 
    ​Details  DOI  PMID  PMC 
  • 2019 | Journal Article
    ​ ​Klein, N., Kneib, T., Marra, G., Radice, R., Rokicki, S. & McGovern, M. E. (2019). ​Mixed binary-continuous copula regression models with application to adverse birth outcomes. Statistics in Medicine, 38(3), ​413​-436​. ​doi: https://doi.org/10.1002/sim.7985 
    ​Details  DOI  PMID  PMC 
  • 2018 | Preprint
    ​ ​Hohberg, M., Pütz, P. & Kneib, T. (2018). Generalized additive models for location, scale and shape for program evaluation: ​A guide to practice.​ Unpublished manuscript. ​
    ​Details 
  • 2018 | Preprint
    ​ ​Säfken, B., Rügamer, D., Kneib, T. & Greven, S. (2018). Conditional Model Selection in Mixed-Effects Models with cAIC4.​ Unpublished manuscript. ​
    ​Details | arXiv 
  • 2018 | Preprint
    ​ ​Hambuckers, J., Groll, A. & Kneib, T. (2018). Understanding the Economic Determinants of the Severity of Operational Losses: A Regularized Generalized Pareto Regression Approach.​ Unpublished manuscript. ​
    ​Details 
  • 2018 | Journal Article
    ​ ​Umlauf, N. & Kneib, T. (2018). ​A primer on Bayesian distributional regression. Statistical Modelling, 18(3-4), ​219​-247​. ​doi: https://doi.org/10.1177/1471082X18759140 
    ​Details  DOI 
  • 2018 | Journal Article
    ​ ​Hambuckers, J., Groll, A. & Kneib, T. (2018). ​Understanding the economic determinants of the severity of operational losses: A regularized generalized Pareto regression approach. Journal of Applied Econometrics, 33(6), ​898​-935​. ​doi: https://doi.org/10.1002/jae.2638 
    ​Details  DOI 
  • 2018 | Journal Article | Editorial Contribution (Editorial, Introduction, Epilogue)
    ​ ​Groll, A., Kneib, T. & Mayr, A. (2018). ​Editorial 'Bridging the gap between methodology and applications: Tutorials on semiparametric regression'. Statistical Modelling, 18(3-4), ​199​-202​. ​doi: https://doi.org/10.1177/1471082X18761252 
    ​Details  DOI 
  • 2018 | Journal Article | Editorial Contribution (Editorial, Introduction, Epilogue)
    ​ ​He, X., Kneib, T., Lamarche, C. & Wang, L. (2018). ​Editorial: Special issue on quantile regression and semiparametric methods. Econometrics and Statistics, 8, ​1​-2​. ​doi: https://doi.org/10.1016/j.ecosta.2018.09.002 
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  • 2018 | Journal Article
    ​ ​Groll, A., Kneib, T., Mayr, A. & Schauberger, G. (2018). ​On the dependency of soccer scores – a sparse bivariate Poisson model for the UEFA European football championship 2016. Journal of Quantitative Analysis in Sports, 14(2), ​65​-79​. ​doi: https://doi.org/10.1515/jqas-2017-0067 
    ​Details  DOI 
  • 2018 | Preprint
    ​ ​Herbst, H., Minnich, A., Herminghaus, S., Kneib, T., Wacker, B. & Schlüter, J. C. (2018). A Behavioral Economic Perspective on Demand Responsive Transportation.​ Unpublished manuscript. ​
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  • 2018 | Journal Article
    ​ ​Ríos-Pena, L., Kneib, T., Cadarso-Suárez, C., Klein, N. & Marey-Pérez, M. (2018). ​Studying the occurrence and burnt area of wildfires using zero-one-inflated structured additive beta regression. Environmental Modelling & Software, 110, ​107​-118​. ​doi: https://doi.org/10.1016/j.envsoft.2018.03.008 
    ​Details  DOI 
  • 2018 | Journal Article
    ​ ​Michaelis, P., Klein, N. & Kneib, T. (2018). ​Bayesian Multivariate Distributional Regression With Skewed Responses and Skewed Random Effects. Journal of Computational and Graphical Statistics, 27(3), ​602​-611​. ​doi: https://doi.org/10.1080/10618600.2017.1395343 
    ​Details  DOI 
  • 2018 | Journal Article
    ​ ​Baumgartner, B., Guhl, D., Kneib, T. & Steiner, W. J. (2018). ​Flexible estimation of time-varying effects for frequently purchased retail goods: a modeling approach based on household panel data. OR Spectrum, 40(4), ​837​-873​. ​doi: https://doi.org/10.1007/s00291-018-0530-6 
    ​Details  DOI 
  • 2018 | Journal Article
    ​ ​Thaden, H. & Kneib, T. (2018). ​Structural Equation Models for Dealing With Spatial Confounding. The American Statistician, 72(3), ​239​-252​. ​doi: https://doi.org/10.1080/00031305.2017.1305290 
    ​Details  DOI 
  • 2018 | Journal Article
    ​ ​Hambuckers, J., Kneib, T., Langrock, R. & Silbersdorff, A. (2018). ​A Markov-switching generalized additive model for compound Poisson processes, with applications to operational loss models. Quantitative Finance, 18(10), ​1679​-1698​. ​doi: https://doi.org/10.1080/14697688.2017.1417625 
    ​Details  DOI 
  • 2018 | Journal Article
    ​ ​Guhl, D., Baumgartner, B., Kneib, T. & Steiner, W. J. (2018). ​Estimating time-varying parameters in brand choice models: A semiparametric approach. International Journal of Research in Marketing, 35(3), ​394​-414​. ​doi: https://doi.org/10.1016/j.ijresmar.2018.03.003 
    ​Details  DOI 
  • 2018 | Journal Article
    ​ ​Espasandín-Domínguez, J., Benítez-Estévez, A. J., Cadarso-Suárez, C., Kneib, T., Barreiro-Martínez, T., Casas-Méndez, B. & Gude, F. (2018). ​Geographical differences in blood potassium detected using a structured additive distributional regression model. Spatial Statistics, 24, ​1​-13​. ​doi: https://doi.org/10.1016/j.spasta.2018.03.001 
    ​Details  DOI 
  • 2018 | Journal Article | 
    ​ ​Hohberg, M., Landau, K., Kneib, T., Klasen, S. & Zucchini, W. (2018). ​Vulnerability to poverty revisited: Flexible modeling and better predictive performance. The Journal of Economic Inequality, ​1​-16​. ​doi: https://doi.org/10.1007/s10888-017-9374-6 
    ​Details  DOI 
  • 2018 | Journal Article
    ​ ​Silbersdorff, A., Lynch, J., Klasen, S. & Kneib, T. (2018). ​Reconsidering the income-health relationship using distributional regression. Health Economics, 27(7), ​1074​-1088​. ​doi: https://doi.org/10.1002/hec.3656 
    ​Details  DOI  PMID  PMC 
  • 2017 | Preprint
    ​ ​Hambuckers, J., Kneib, T., Langrock, R. & Silbersdorff, A. (2017). A Markov-Switching Generalized Additive Model for Compound Poisson Processes, with Applications to Operational Losses Models.​ Unpublished manuscript. ​
    ​Details 
  • 2017 | Preprint
    ​ ​Adam, T., Mayr, A. & Kneib, T. (2017). Gradient boosting in Markov-switching generalized additive models for location, scale and shape.​ Unpublished manuscript. ​
    ​Details | arXiv 
  • 2017 | Preprint
    ​ ​Mascarenhas, A., Marques, F., Silva, S., Gouveia, S., Alves, M., Virella, D., Papoila, A. L. ... Neto, M. T. (2017). Determinants of the Variability of Oxygen Saturation during the First Minutes of Life of Term Neonates.​ Unpublished manuscript. ​
    ​Details 
  • 2017 | Journal Article
    ​ ​Spiegel, E., Kneib, T. & Otto-Sobotka, F. (2017). ​Generalized additive models with flexible response functions. Statistics and Computing, 29(1), ​123​-138​. ​doi: https://doi.org/10.1007/s11222-017-9799-6 
    ​Details  DOI 
  • 2017 | Journal Article
    ​ ​Pütz, P. & Kneib, T. (2017). ​A penalized spline estimator for fixed effects panel data models. Advances in Statistical Analysis, 102(2), ​145​-166​. ​doi: https://doi.org/10.1007/s10182-017-0296-1 
    ​Details  DOI 
  • 2017 | Journal Article | 
    ​ ​Spiegel, E., Sobotka, F. & Kneib, T. (2017). ​Model selection in semiparametric expectile regression. Electronic Journal of Statistics, 11(2), ​3008​-3038​. ​doi: https://doi.org/10.1214/17-EJS1307 
    ​Details  DOI 
  • 2017 | Journal Article | 
    ​ ​Idzalika, R., Kneib, T. & Martinez-Zarzoso, I. (2017). ​The effect of income on democracy revisited a flexible distributional approach. Empirical Economics, 56(4), ​1207​-1230​. ​doi: https://doi.org/10.1007/s00181-017-1390-7 
    ​Details  DOI 
  • 2017 | Journal Article | 
    ​ ​Langrock, R., Kneib, T., Glennie, R. & Michelot, T. (2017). ​Markov-switching generalized additive models. Statistics and Computing, 27(1), ​259​-270​. ​doi: https://doi.org/10.1007/s11222-015-9620-3 
    ​Details  DOI 
  • 2017 | Journal Article
    ​ ​Ríos-Pena, L., Kneib, T., Cadarso-Suárez, C. & Marey-Pérez, M. (2017). ​Predicting the occurrence of wildfires with binary structured additive regression models. Journal of Environmental Management, 187, ​154​-165​. ​doi: https://doi.org/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. (2017). ​Editorial "Joint modeling of longitudinal and time-to-event data and beyond". Biometrical Journal, 59(6), ​1101​-1103​. ​doi: https://doi.org/10.1002/bimj.201700180 
    ​Details  DOI  PMID  PMC 
  • 2017 | Journal Article
    ​ ​Mamouridis, V., Klein, N., Kneib, T., Cadarso Suarez, C. & Maynou, F. (2017). ​Structured additive distributional regression for analysing landings per unit effort in fisheries research. Mathematical Biosciences, 283, ​145​-154​. ​doi: https://doi.org/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., Gerullis, H. ... Wawroschek, F. (2017). ​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, 8(14), ​2692​-2698​. ​doi: https://doi.org/10.7150/jca.20409 
    ​Details  DOI  PMID  PMC 
  • 2016 | Journal Article
    ​ ​März, A., Klein, N., Kneib, T. & Mußhoff, O. (2016). ​Analysing farmland rental rates using Bayesian geoadditive quantile regression. European Review of Agricultural Economics, 43(4), ​663​-698​. ​doi: https://doi.org/10.1093/erae/jbv028 
    ​Details  DOI 
  • 2016 | Journal Article
    ​ ​Kneib, T. (2016). ​Smoothing Parameter and Model Selection for General Smooth Models Comment. Journal of the American Statistical Association, 111(516), ​1563​-1565​. ​doi: https://doi.org/10.1080/01621459.2016.1250576 
    ​Details  DOI  WoS 
  • 2016 | Journal Article | Research Paper
    ​ ​Sohn, A., Klein, N. & Kneib, T. (2016). ​A Semiparametric Analysis of Conditional Income Distributions. Schmollers Jahrbuch, 135(1), ​13​-22​. ​doi: https://doi.org/10.3790/schm.135.1.13 
    ​Details  DOI 
  • 2016 | Journal Article | Erratum | 
    ​ ​Klein, N., Kneib, T., Lang, S. & Sohn, A. (2016). ​Correction: Bayesian structured additive distributional regression with an application to regional income inequality in Germany. The Annals of Applied Statistics, 10(2), ​1135​-1136​. ​doi: https://doi.org/10.1214/16-AOAS922 
    ​Details  DOI 
  • 2016 | Journal Article | 
    ​ ​Álvaro-Meca, A., Jiménez-Sousa, M. A., Boyer, A., Medrano, J., Reulen, H., Kneib, T. & Resino, S. (2016). ​Impact of chronic hepatitis C on mortality in cirrhotic patients admitted to intensive-care unit. BMC Infectious Diseases, 16(1), Article 122​. ​doi: https://doi.org/10.1186/s12879-016-1448-8 
    ​Details  DOI 
  • 2016 | Journal Article | 
    ​ ​Tahden, M., Manitz, J., Baumgardt, K., Fell, G., Kneib, T. & Hegasy, G. (2016). ​Epidemiological and Ecological Characterization of the EHEC O104:H4 Outbreak in Hamburg, Germany, 2011. PLOS ONE, 11(10), Article e0164508​. ​doi: https://doi.org/10.1371/journal.pone.0164508 
    ​Details  DOI  PMID  PMC 
  • 2016 | Journal Article
    ​ ​Sennhenn-Reulen, H. & Kneib, T. (2016). ​Structured fusion lasso penalized multi-state models. Statistics in Medicine, 35(25), ​4637​-4659​. ​doi: https://doi.org/10.1002/sim.7017 
    ​Details  DOI  PMID  PMC 
  • 2015 | Review
    ​ ​Kneib, T. (2015). ​Applied Statistical Inference: Likelihood and Bayes. L.Held and D.Sabanés Bové (2014). Heidelberg: Springer. 376 pages, ISBN: 3642378862​ [Review of Applied Statistical Inference: Likelihood and Bayes]. ​Biometrical Journal, 57(2), 362​-363​. ​doi: https://doi.org/10.1002/bimj.201400209 
    ​Details  DOI 
  • 2015 | Journal Article
    ​ ​Waltrup, L. S., Sobotka, F., Kneib, T. & Kauermann, G. (2015). ​Expectile and quantile regression-David and Goliath? Statistical Modelling, 15(5), ​433​-456​. ​doi: https://doi.org/10.1177/1471082X14561155 
    ​Details  DOI  WoS 
  • 2015 | Journal Article | 
    ​ ​Umlauf, N., Adler, D., Kneib, T., Lang, S. & Zeileis, A. (2015). ​Structured Additive Regression Models: An R Interface to BayesX. Journal of Statistical Software, 63(21), ​1​-46​. ​doi: https://doi.org/10.18637/jss.v063.i21 
    ​Details  DOI 
  • 2015 | Journal Article | 
    ​ ​Ríos-Pena, L., Cadarso-Suárez, C., Kneib, T. & Pérez, M. (2015). ​Applying Binary Structured Additive Regression (STAR) for Predicting Wildfire in Galicia, Spain. Procedia Environmental Sciences, 27, ​123​-126​. ​doi: https://doi.org/10.1016/j.proenv.2015.07.121 
    ​Details  DOI 
  • 2015 | Journal Article | 
    ​ ​Winter, A., Kneib, T., Rohde, M., Henke, Rolf-Peter & Wawroschek, F. (2015). ​First Nomogram Predicting the Probability of Lymph Node Involvement in Prostate Cancer Patients Undergoing Radioisotope Guided Sentinel Lymph Node Dissection. Urologia Internationalis, 95(4), ​422​-428​. ​doi: https://doi.org/10.1159/000431182 
    ​Details  DOI  PMID  PMC 
  • 2014 | Journal Article
    ​ ​Manitz, J., Kneib, T., Schlather, M., Helbing, D. & Brockmann, D. (2014). ​Origin Detection During Food-borne Disease Outbreaks - A Case Study of the 2011 EHEC/HUS Outbreak in Germany. PLoS Currents, . ​doi: https://doi.org/10.1371/currents.outbreaks.f3fdeb08c5b9de7c09ed9cbcef5f01f2 
    ​Details  DOI  PMID  PMC 
  • 2014 | Report
    ​ ​Sohn, A., Klein, N. & Kneib, T. (2014). A New Semiparametric Approach to Analysing Conditional Income Distributions​​. ​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. (2014). ​A unifying approach to the estimation of the conditional Akaike information in generalized linear mixed models. Electronic Journal of Statistics, 8(1), ​201​-225​. ​doi: https://doi.org/10.1214/14-EJS881 
    ​Details  DOI 
  • 2014 | Journal Article | 
    ​ ​Helms, Hans-Joachim, Benda, N., Zinserling, J., Kneib, T. & Friede, T. (2014). ​Spline-based procedures for dose-finding studies with active control. Statistics in Medicine, 34(2), ​232​-248​. ​doi: https://doi.org/10.1002/sim.6320 
    ​Details  DOI  PMID  PMC 
  • 2014 | Journal Article | 
    ​ ​Freytag, S., Manitz, J., Schlather, M., Kneib, T., Amos, C. I., Risch, A., Chang-Claude, J. ... Bickeböller, H. (2014). ​A Network-Based Kernel Machine Test for the Identification of Risk Pathways in Genome-Wide Association Studies. Human Heredity, 76(2), ​64​-75​. ​doi: https://doi.org/10.1159/000357567 
    ​Details  DOI  PMID  PMC 
  • 2014 | Journal Article
    ​ ​Bühlmann, P., Gertheiss, J., Hieke, S., Kneib, T., Ma, S., Schumacher, M., Tutz, G. ... Ziegler, A. (2014). ​Discussion of "The Evolution of Boosting Algorithms" and "Extending Statistical Boosting". Methods of Information in Medicine, 53(6), ​436​-445​. ​doi: https://doi.org/10.3414/13100122 
    ​Details  DOI  PMID  PMC  WoS 
  • 2013 | Preprint
    ​ ​Langrock, R., Kneib, T., Sohn, A. & DeRuiter, S. (2013). Nonparametric inference in hidden Markov models using P-splines.​ Unpublished manuscript. ​
    ​Details | arXiv 
  • 2013 | Preprint
    ​ ​Langrock, R., Michelot, T., Sohn, A. & Kneib, T. (2013). Semiparametric stochastic volatility modelling using penalized splines.​ Unpublished manuscript. ​
    ​Details | arXiv 
  • 2013 | Journal Article
    ​ ​Rodríguez-Girondo, M., Kneib, T., Cadarso-Suárez, C. & Abu-Assi, E. (2013). ​Model building in nonproportional hazard regression. Statistics in Medicine, 32(30), ​5301​-5314​. ​doi: https://doi.org/10.1002/sim.5961 
    ​Details  DOI 
  • 2013 | Journal Article
    ​ ​Kneib, T. (2013). ​Rejoinder. Statistical Modelling, 13(4), ​373​-385​. ​doi: https://doi.org/10.1177/1471082X13494531 
    ​Details  DOI  WoS 
  • 2013 | Journal Article | 
    ​ ​Kneib, T. (2013). ​Beyond mean regression. Statistical Modelling, 13(4), ​275​-303​. ​doi: https://doi.org/10.1177/1471082X13494159 
    ​Details  DOI  WoS 
  • 2013 | Journal Article | 
    ​ ​Yue, Y. R., Lang, S., Flexeder, C., Waldmann, E. & Kneib, T. (2013). ​Bayesian semiparametric additive quantile regression. Statistical Modelling, 13(3), ​223​-252​. ​doi: https://doi.org/10.1177/1471082x13480650 
    ​Details  DOI 
  • 2013 | Journal Article | 
    ​ ​Freytag, S., Bickeböller, H., Amos, C. I., Kneib, T. & Schlather, M. (2013). ​A Novel Kernel for Correcting Size Bias in the Logistic Kernel Machine Test with an Application to Rheumatoid Arthritis. Human Heredity, 74(2), ​97​-108​. ​doi: https://doi.org/10.1159/000347188 
    ​Details  DOI  PMID  PMC 
  • 2012 | Journal Article
    ​ ​Mehr, M., Brandl, R., Kneib, T. & Müller, J. (2012). ​The effect of bark beetle infestation and salvage logging on bat activity in a national park. Biodiversity and Conservation, 21(11), ​2775​-2786​. ​doi: https://doi.org/10.1007/s10531-012-0334-y 
    ​Details  DOI 
  • 2012 | Conference Abstract
    ​ ​Freytag, S., Amos, C. I., Bickeböller, H., Kneib, T. & Schlather, M. (2012). ​Novel Kernel Function in the Logistic Kernel Machine Test for Pathways in GWA Studies.​ Genetic Epidemiology​, 36(7)
    ​Details  WoS 
  • 2011 | Journal Article | 
    ​ ​Sobotka, F., Kauermann, G., Schulze Waltrup, L. & Kneib, T. (2011). ​On confidence intervals for semiparametric expectile regression. Statistics and Computing, 23(2), ​135​-148​. ​doi: https://doi.org/10.1007/s11222-011-9297-1 
    ​Details  DOI 

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