Prof. Dr. Thomas Kneib

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  • 2023 | Preprint
    ​ ​"Spatial Joint Models through Bayesian Structured Piece-wise Additive Joint Modelling for Longitudinal and Time-to-Event Data"​
    Rappl, A.; Kneib, T. ; Lang, S.& Bergherr, E. ​ (2023)
    ​Details 
  • 2022 | Journal Article | Research Paper | 
    ​ ​Is age at menopause decreasing? – The consequences of not completing the generational cohort​
    Martins, R.; Sousa, B. d.; Kneib, T. ; Hohberg, M. ; Klein, N. ; Duarte, E. & Rodrigues, V.​ (2022) 
    BMC Medical Research Methodology, 22(1) art. 187​.​ DOI: https://doi.org/10.1186/s12874-022-01658-x 
    ​Details  DOI 
  • 2022 | Journal Article
    ​ ​Correcting for sample selection bias in Bayesian distributional regression models​
    Wiemann, P. F.; Klein, N. & Kneib, T. ​ (2022) 
    Computational Statistics & Data Analysis, 168 art. S0167947321002164​.​ DOI: https://doi.org/10.1016/j.csda.2021.107382 
    ​Details  DOI 
  • 2022 | Journal Article | 
    ​ ​Mapping ex ante risks of COVID‐19 in Indonesia using a Bayesian geostatistical model on airport network data​
    Seufert, J. D.; Python, A.; Weisser, C.; Cisneros, E. ; Kis-Katos, K.   & Kneib, T. ​ (2022) 
    Journal of the Royal Statistical Society: Series A (Statistics in Society), art. rssa.12866​.​ DOI: https://doi.org/10.1111/rssa.12866 
    ​Details  DOI 
  • 2022 | Journal Article | 
    ​ ​Generalised exponential-Gaussian distribution: a method for neural reaction time analysis​
    Marmolejo-Ramos, F.; Barrera-Causil, C.; Kuang, S.; Fazlali, Z.; Wegener, D.; Kneib, T.   & De Bastiani, F. et al.​ (2022) 
    Cognitive Neurodynamics,.​ DOI: https://doi.org/10.1007/s11571-022-09813-2 
    ​Details  DOI 
  • 2022 | Journal Article | Research Paper | 
    ​ ​Mitigating spatial confounding by explicitly correlating Gaussian random fields​
    Marques, I. ; Kneib, T.   & Klein, N.​ (2022) 
    Environmetrics, 33(5).​ DOI: https://doi.org/10.1002/env.2727 
    ​Details  DOI 
  • 2022 | Journal Article | 
    ​ ​Pseudo-document simulation for comparing LDA, GSDMM and GPM topic models on short and sparse text using Twitter data​
    Weisser, C.; Gerloff, C.; Thielmann, A.; Python, A.; Reuter, A.; Kneib, T.   & Säfken, B.​ (2022) 
    Computational Statistics,.​ DOI: https://doi.org/10.1007/s00180-022-01246-z 
    ​Details  DOI 
  • 2022 | Journal Article | 
    ​ ​A non-stationary model for spatially dependent circular response data based on wrapped Gaussian processes​
    Marques, I. ; Kneib, T.   & Klein, N. ​ (2022) 
    Statistics and Computing, 32(5).​ DOI: https://doi.org/10.1007/s11222-022-10136-9 
    ​Details  DOI 
  • 2022 | Journal Article | 
    ​ ​Bayesian discrete conditional transformation models​
    Carlan, M. & Kneib, T. ​ (2022) 
    Statistical Modelling, art. 1471082X2211141​.​ DOI: https://doi.org/10.1177/1471082X221114177 
    ​Details  DOI 
  • 2022 | Journal Article | 
    ​ ​Distributional regression modeling via generalized additive models for location, scale, and shape: An overview through a data set from learning analytics​
    Marmolejo‐Ramos, F.; Tejo, M.; Brabec, M.; Kuzilek, J.; Joksimovic, S.; Kovanovic, V. & González, J. et al.​ (2022) 
    Wiley Interdisciplinary Reviews. Data Mining and Knowledge Discovery,.​ DOI: https://doi.org/10.1002/widm.1479 
    ​Details  DOI 
  • 2022 | Journal Article
    ​ ​In memory of Carmen María Cadarso Suárez (1960–2022)​
    Melis, G. G. & Kneib, T. ​ (2022) 
    Biometrical Journal, 64(7) pp. 1159​-1160​.​ DOI: https://doi.org/10.1002/bimj.202270075 
    ​Details  DOI 
  • 2021 | Journal Article | Research Paper | 
    ​ ​Introductory data science across disciplines, using Python, case studies, and industry consulting projects​
    Lasser, J.; Manik, D.; Silbersdorff, A. ; Säfken, B. & Kneib, T. ​ (2021) 
    Teaching Statistics, 43 pp. S190​-S200​.​ DOI: https://doi.org/10.1111/test.12243 
    ​Details  DOI 
  • 2021 | Journal Article
    ​ ​Smooth-Transition Regression Models for Non-Stationary Extremes​
    Hambuckers, J. & Kneib, T. ​ (2021) 
    Journal of Financial Econometrics,.​ DOI: https://doi.org/10.1093/jjfinec/nbab005 
    ​Details  DOI 
  • 2021 | Journal Article
    ​ ​Conditional Model Selection in Mixed-Effects Models with cAIC4​
    Säfken, B.; Rügamer, D.; Kneib, T.   & Greven, S.​ (2021) 
    Journal of Statistical Software, 99(8).​ DOI: https://doi.org/10.18637/jss.v099.i08 
    ​Details  DOI 
  • 2021 | Journal Article
    ​ ​Generalized expectile regression with flexible response function​
    Spiegel, E. ; Kneib, T. ; von Gablenz, P. & Otto‐Sobotka, F.​ (2021) 
    Biometrical Journal,.​ DOI: https://doi.org/10.1002/bimj.202000203 
    ​Details  DOI 
  • 2021 | Journal Article
    ​ ​Modelling children's anthropometric status using Bayesian distributional regression merging socio-economic and remote sensed data from South Asia and sub-Saharan Africa​
    Seiler, J.; Harttgen, K.; Kneib, T.   & Lang, S.​ (2021) 
    Economics and Human Biology, 40 pp. 100950​.​ DOI: https://doi.org/10.1016/j.ehb.2020.100950 
    ​Details  DOI 
  • 2021 | Journal Article
    ​ ​Bayesian Effect Selection in Structured Additive Distributional Regression Models​
    Klein, N.; Carlan, M.; Kneib, T. ; Lang, S. & Wagner, H.​ (2021) 
    Bayesian Analysis, 16(2).​ DOI: https://doi.org/10.1214/20-BA1214 
    ​Details  DOI 
  • 2021 | Journal Article | Research Paper
    ​ ​Predicting Tree Species From 3D Laser Scanning Point Clouds Using Deep Learning​
    Seidel, D. ; Annighöfer, P. ; Thielman, A.; Seifert, Q. E. ; Thauer, J.-H.; Glatthorn, J. & Ehbrecht, M. et al.​ (2021) 
    Frontiers in Plant Science, 12.​ DOI: https://doi.org/10.3389/fpls.2021.635440 
    ​Details  DOI 
  • 2021 | Journal Article | Research Paper | 
    ​ ​Environmental heterogeneity predicts global species richness patterns better than area​
    Udy, K.; Fritsch, M. ; Meyer, K. M. ; Grass, I. ; Hanß, S. ; Hartig, F. & Kneib, T.  et al.​ (2021) 
    Global Ecology and Biogeography, 30(4) pp. 842​-851​.​ DOI: https://doi.org/10.1111/geb.13261 
    ​Details  DOI 
  • 2021 | Journal Article | 
    ​ ​Interactively visualizing distributional regression models with distreg.vis​
    Stadlmann, S. & Kneib, T. ​ (2021) 
    Statistical Modelling, 22(6) pp. 527​-545​.​ DOI: https://doi.org/10.1177/1471082X211007308 
    ​Details  DOI 
  • 2021 | Journal Article | Research Paper | 
    ​ ​Beyond unidimensional poverty analysis using distributional copula models for mixed ordered‐continuous outcomes​
    Hohberg, M. ; Donat, F.; Marra, G. & Kneib, T. ​ (2021) 
    Journal of the Royal Statistical Society: Series C (Applied Statistics), 70(5) pp. 1365​-1390​.​ DOI: https://doi.org/10.1111/rssc.12517 
    ​Details  DOI 
  • 2020 | Journal Article | 
    ​ ​Spatio-temporal expectile regression models​
    Kneib, T. ; Otto-Sobotka, F. & Spiegel, E.​ (2020) 
    Statistical Modelling, 20(4) art. 1471082X1982994​.​ DOI: https://doi.org/10.1177/1471082X19829945 
    ​Details  DOI 
  • 2020 | Journal Article
    ​ ​Mixed Discrete‐Continuous Regression – A Novel Approach Based on Weight Functions​
    Michaelis, P.; Klein, N. & Kneib, T. ​ (2020) 
    Stat,.​ DOI: https://doi.org/10.1002/sta4.277 
    ​Details  DOI 
  • 2020 | Preprint
    ​ ​Application of an additive structured copula regression on the joint wind speed and wind direction distribution​
    Seebaß, J. V.; Schlüter, J.; Wacker, B.& Kneib, T. ​ (2020)
    ​Details 
  • 2020 | Preprint
    ​ ​On Existence and Uniqueness of Maximum Log-Likelihood Parameter Estimation for Two-Parameter Weibull Distributions​
    Wacker, B.; Kneib, T.  & Schlüter, J.​ (2020)
    ​Details 
  • 2020 | Report
    ​ ​Smooth Transition Regression Models for Non-Stationary Extremes​
    Hambuckers, J.& Kneib, T. ​ (2020). DOI: https://doi.org/10.2139/ssrn.3541718 
    ​Details  DOI 
  • 2020 | Journal Article
    ​ ​Modelling regional patterns of inefficiency: A Bayesian approach to geoadditive panel stochastic frontier analysis with an application to cereal production in England and Wales​
    Klein, N.; Herwartz, H. & Kneib, T. ​ (2020) 
    Journal of Econometrics, 214(2) pp. 513​-539​.​ DOI: https://doi.org/10.1016/j.jeconom.2019.07.003 
    ​Details  DOI 
  • 2020 | Journal Article
    ​ ​Multivariate conditional transformation models​
    Klein, N. ; Hothorn, T.; Barbanti, L. & Kneib, T. ​ (2020) 
    Scandinavian Journal of Statistics,.​ DOI: https://doi.org/10.1111/sjos.12501 
    ​Details  DOI 
  • 2020 | Book Chapter
    ​ ​Bayesian mixed binary-continuous copula regression with an application to childhood undernutrition​
    Klein, N.; Kneib, T. ; Marra, G.& Radice, R.​ (2020)
    In:​Dortet-Bernadet, Jean-Luc; Fan, Yanan; Nott, David; Smith, Mike S.​ (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
    ​ ​Noncrossing structured additive multiple-output Bayesian quantile regression models​
    Santos, B. & Kneib, T. ​ (2020) 
    Statistics and Computing,.​ DOI: https://doi.org/10.1007/s11222-020-09925-x 
    ​Details  DOI 
  • 2020 | Conference Paper
    ​ ​Towards a Taxonomy for Data Heterogeneity​
    Roeder, J. ; Muntermann, J.   & Kneib, T. ​ (2020)
    ​Proceedings of Internationale Tagung Wirtschaftsinformatik 2020. ​Internationale Tagung Wirtschaftsinformatik 2020​, Potsdam.
    ​Details 
  • 2020 | Journal Article
    ​ ​Bayesian Gaussian distributional regression models for more efficient norm estimation​
    Voncken, L.; Kneib, T. ; Albers, C. J.; Umlauf, N. & Timmerman, M. E.​ (2020) 
    British Journal of Mathematical and Statistical Psychology, 74(1) pp. 99​-117​.​ DOI: https://doi.org/10.1111/bmsp.12206 
    ​Details  DOI 
  • 2020 | Journal Article | Research Paper
    ​ ​Non-stationary spatial regression for modelling monthly precipitation in Germany​
    Marques, I. ; Klein, N. & Kneib, T. ​ (2020) 
    Spatial Statistics, 40 art. 100386​.​ DOI: https://doi.org/10.1016/j.spasta.2019.100386 
    ​Details  DOI 
  • 2020 | Journal Article
    ​ ​Flexible instrumental variable distributional regression​
    Briseño Sanchez, G.; Hohberg, M. ; Groll, A.   & Kneib, T. ​ (2020) 
    Journal of the Royal Statistical Society: Series A (Statistics in Society), 183(4) pp. 1553​-1574​.​ DOI: https://doi.org/10.1111/rssa.12598 
    ​Details  DOI 
  • 2020 | Journal Article | 
    ​ ​Comments on: Inference and computation with Generalized Additive Models and their extensions​
    Kneib, T. ​ (2020) 
    TEST, 29(2) pp. 351​-353​.​ DOI: https://doi.org/10.1007/s11749-020-00713-3 
    ​Details  DOI 
  • 2020 | Journal Article | 
    ​ ​Generalised joint regression for count data: a penalty extension for competitive settings​
    van der Wurp, H.; Groll, A. ; Kneib, T. ; Marra, G. & Radice, R.​ (2020) 
    Statistics and Computing, 30(5) pp. 1419​-1432​.​ DOI: https://doi.org/10.1007/s11222-020-09953-7 
    ​Details  DOI 
  • 2020 | Journal Article | Editorial Contribution (Editorial, Introduction, Epilogue) | 
    ​ ​Editorial​
    Kauermann, G.; Kneib, T.   & Okhrin, Y.​ (2020) 
    Advances in Statistical Analysis, 104(1) pp. 1​-3​.​ DOI: https://doi.org/10.1007/s10182-020-00361-w 
    ​Details  DOI 
  • 2020 | Journal Article | 
    ​ ​Treatment effects beyond the mean using distributional regression: Methods and guidance​
    Hohberg, M. ; Pütz, P. & Kneib, T. ​ (2020) 
    PLoS One, 15(2) art. e0226514​.​ DOI: https://doi.org/10.1371/journal.pone.0226514 
    ​Details  DOI  PMID  PMC 

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