Prof. Dr. Thomas Kneib

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  • 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 | 
    ​ ​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 
  • 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 | 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 | 
    ​ ​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 
  • 2019 | Journal Article | 
    ​ ​Rocks rock: the importance of rock formations as resting sites of the Eurasian lynx Lynx lynx​
    Signer, J. ; Filla, M.; Schoneberg, S.; Kneib, T. ; Bufka, L.; Belotti, E. & Heurich, M.​ (2019) 
    Wildlife Biology, 2019(1).​ DOI: https://doi.org/10.2981/wlb.00489 
    ​Details  DOI 
  • 2019 | Journal Article | Research Paper | 
    ​ ​Reducing Fertilizer and Avoiding Herbicides in Oil Palm Plantations - Ecological and Economic Valuations​
    Darras, K. F. A. ; Corre, M. D. ; Formaglio, G.; Tjoa, A.; Potapov, A. ; Brambach, F.   & Sibhatu, K. T.  et al.​ (2019) 
    Frontiers in Forests and Global Change, 2.​ DOI: https://doi.org/10.3389/ffgc.2019.00065 
    ​Details  DOI 
  • 2019 | Journal Article | 
    ​ ​Conditional covariance penalties for mixed models​
    Säfken, B. & Kneib, T. ​ (2019) 
    Scandinavian Journal of Statistics, 47(3) pp. 990​-1010​.​ DOI: https://doi.org/10.1111/sjos.12437 
    ​Details  DOI 
  • 2018 | Journal Article | 
    ​ ​Vulnerability to poverty revisited: Flexible modeling and better predictive performance​
    Hohberg, M. ; Landau, K.; Kneib, T. ; Klasen, S.   & Zucchini, W. ​ (2018) 
    The Journal of Economic Inequality, pp. 1​-16​.​ DOI: https://doi.org/10.1007/s10888-017-9374-6 
    ​Details  DOI 
  • 2017 | Journal Article | 
    ​ ​Model selection in semiparametric expectile regression​
    Spiegel, E. ; Sobotka, F. & Kneib, T. ​ (2017) 
    Electronic Journal of Statistics, 11(2) pp. 3008​-3038​.​ DOI: https://doi.org/10.1214/17-EJS1307 
    ​Details  DOI 
  • 2017 | Journal Article | 
    ​ ​The effect of income on democracy revisited a flexible distributional approach​
    Idzalika, R.; Kneib, T.   & Martinez-Zarzoso, I.​ (2017) 
    Empirical Economics, 56(4) pp. 1207​-1230​.​ DOI: https://doi.org/10.1007/s00181-017-1390-7 
    ​Details  DOI 
  • 2017 | Journal Article | 
    ​ ​Markov-switching generalized additive models​
    Langrock, R. ; Kneib, T. ; Glennie, R. & Michelot, T.​ (2017) 
    Statistics and Computing, 27(1) pp. 259​-270​.​ DOI: https://doi.org/10.1007/s11222-015-9620-3 
    ​Details  DOI 
  • 2017 | Journal Article | 
    ​ ​Updated Nomogram Incorporating Percentage of Positive Cores to Predict Probability of Lymph Node Invasion in Prostate Cancer Patients Undergoing Sentinel Lymph Node Dissection​
    Winter, A.; Kneib, T. ; Wasylow, C.; Reinhardt, L.; Henke, R.-P.; Engels, S. & Gerullis, H. et al.​ (2017) 
    Journal of Cancer, 8(14) pp. 2692​-2698​.​ DOI: https://doi.org/10.7150/jca.20409 
    ​Details  DOI  PMID  PMC 
  • 2017 | Journal Article | 
    ​ ​Pathway-Based Kernel Boosting for the Analysis of Genome-Wide Association Studies​
    Friedrichs, S. ; Manitz, J. ; Burger, P.; Amos, C. I.; Risch, A.; Chang-Claude, J. & Wichmann, H.-E. et al.​ (2017) 
    Computational and mathematical methods in medicine, 2017 pp. 6742763​-17​.​ DOI: https://doi.org/10.1155/2017/6742763 
    ​Details  DOI  PMID  PMC 
  • 2016 | Journal Article | Erratum | 
    ​ ​Correction: Bayesian structured additive distributional regression with an application to regional income inequality in Germany​
    Klein, N. ; Kneib, T. ; Lang, S. & Sohn, A. ​ (2016) 
    The Annals of Applied Statistics, 10(2) pp. 1135​-1136​.​ DOI: https://doi.org/10.1214/16-AOAS922 
    ​Details  DOI 
  • 2016 | Journal Article | 
    ​ ​Impact of chronic hepatitis C on mortality in cirrhotic patients admitted to intensive-care unit​
    Álvaro-Meca, A.; Jiménez-Sousa, M. A.; Boyer, A.; Medrano, J.; Reulen, H.; Kneib, T.   & Resino, S.​ (2016) 
    BMC Infectious Diseases, 16(1) art. 122​.​ DOI: https://doi.org/10.1186/s12879-016-1448-8 
    ​Details  DOI 
  • 2016 | Journal Article | 
    ​ ​Epidemiological and Ecological Characterization of the EHEC O104:H4 Outbreak in Hamburg, Germany, 2011​
    Tahden, M.; Manitz, J. ; Baumgardt, K.; Fell, G.; Kneib, T.   & Hegasy, G.​ (2016) 
    PLOS ONE, 11(10) art. e0164508​.​ DOI: https://doi.org/10.1371/journal.pone.0164508 
    ​Details  DOI  PMID  PMC 
  • 2015 | Journal Article | 
    ​ ​Assessing opportunities for physical activity in the built environment of children: interrelation between kernel density and neighborhood scale​
    Buck, C.; Kneib, T. ; Tkaczick, T.; Konstabel, K. & Pigeot, I.​ (2015) 
    International Journal of Health Geographics, 14(1).​ DOI: https://doi.org/10.1186/s12942-015-0027-3 
    ​Details  DOI 
  • 2015 | Journal Article | 
    ​ ​Structured Additive Regression Models: An R Interface to BayesX​
    Umlauf, N.; Adler, D.; Kneib, T. ; Lang, S. & Zeileis, A.​ (2015) 
    Journal of Statistical Software, 63(21) pp. 1​-46​.​ DOI: https://doi.org/10.18637/jss.v063.i21 
    ​Details  DOI 
  • 2015 | Journal Article | 
    ​ ​Applying Binary Structured Additive Regression (STAR) for Predicting Wildfire in Galicia, Spain​
    Ríos-Pena, L.; Cadarso-Suárez, C.; Kneib, T.   & Pérez, M.​ (2015) 
    Procedia Environmental Sciences, 27 pp. 123​-126​.​ DOI: https://doi.org/10.1016/j.proenv.2015.07.121 
    ​Details  DOI 
  • 2015 | Journal Article | 
    ​ ​First Nomogram Predicting the Probability of Lymph Node Involvement in Prostate Cancer Patients Undergoing Radioisotope Guided Sentinel Lymph Node Dissection​
    Winter, A.; Kneib, T. ; Rohde, M.; Henke, R.-P. & Wawroschek, F.​ (2015) 
    Urologia Internationalis, 95(4) pp. 422​-428​.​ DOI: https://doi.org/10.1159/000431182 
    ​Details  DOI  PMID  PMC 
  • 2014 | Journal Article | Research Paper | 
    ​ ​A unifying approach to the estimation of the conditional Akaike information in generalized linear mixed models​
    Saefken, B.; Kneib, T. ; van Waveren, C.-S. & Greven, S.​ (2014) 
    Electronic Journal of Statistics, 8(1) pp. 201​-225​.​ DOI: https://doi.org/10.1214/14-EJS881 
    ​Details  DOI 
  • 2014 | Journal Article | 
    ​ ​Spline-based procedures for dose-finding studies with active control​
    Helms, H.-J.; Benda, N.; Zinserling, J.; Kneib, T.   & Friede, T. ​ (2014) 
    Statistics in Medicine, 34(2) pp. 232​-248​.​ DOI: https://doi.org/10.1002/sim.6320 
    ​Details  DOI  PMID  PMC 
  • 2014 | Journal Article | 
    ​ ​A Network-Based Kernel Machine Test for the Identification of Risk Pathways in Genome-Wide Association Studies​
    Freytag, S.; Manitz, J. ; Schlather, M.; Kneib, T. ; Amos, C. I.; Risch, A. & Chang-Claude, J. et al.​ (2014) 
    Human Heredity, 76(2) pp. 64​-75​.​ DOI: https://doi.org/10.1159/000357567 
    ​Details  DOI  PMID  PMC 
  • 2013 | Journal Article | 
    ​ ​Beyond mean regression​
    Kneib, T. ​ (2013) 
    Statistical Modelling, 13(4) pp. 275​-303​.​ DOI: https://doi.org/10.1177/1471082X13494159 
    ​Details  DOI  WoS 
  • 2013 | Journal Article | 
    ​ ​Bayesian semiparametric additive quantile regression​
    Yue, Y. R.; Lang, S.; Flexeder, C.; Waldmann, E.   & Kneib, T. ​ (2013) 
    Statistical Modelling, 13(3) pp. 223​-252​.​ DOI: https://doi.org/10.1177/1471082x13480650 
    ​Details  DOI 
  • 2013 | Review | 
    ​ ​Gerhard Tutz: „Regression for Categorical Data“​
    Kneib, T. ​ (2013)
    Jahresbericht der Deutschen Mathematiker-Vereinigung, 115​(1) pp. 51​-55​.​ DOI: https://doi.org/10.1365/s13291-013-0058-2 
    ​Details  DOI 
  • 2013 | Journal Article | 
    ​ ​A Novel Kernel for Correcting Size Bias in the Logistic Kernel Machine Test with an Application to Rheumatoid Arthritis​
    Freytag, S.; Bickeböller, H. ; Amos, C. I.; Kneib, T.   & Schlather, M.​ (2013) 
    Human Heredity, 74(2) pp. 97​-108​.​ DOI: https://doi.org/10.1159/000347188 
    ​Details  DOI  PMID  PMC 
  • 2011 | Journal Article | 
    ​ ​Comparison of a Bayesian and a regression model for stimulus classification​
    Köpcke, L. S; Furche, J.; Juárez Paz, L. M; Kneib, T.   & Kretzberg, J.​ (2011) 
    BMC Neuroscience, 12(S1).​ DOI: https://doi.org/10.1186/1471-2202-12-S1-P178 
    ​Details  DOI 
  • 2011 | Journal Article | 
    ​ ​On confidence intervals for semiparametric expectile regression​
    Sobotka, F.; Kauermann, G.; Schulze Waltrup, L. & Kneib, T. ​ (2011) 
    Statistics and Computing, 23(2) pp. 135​-148​.​ DOI: https://doi.org/10.1007/s11222-011-9297-1 
    ​Details  DOI 
  • 2010 | Journal Article | 
    ​ ​Model-based Boosting 2.0​
    Hothorn, T.; Bühlmann, P.; Kneib, T. ; Schmid, M. & Hofner, B.​ (2010) 
    Journal of Machine Learning Reseach - Machine Learning Open Source Software, 11 pp. 2109​-2113​.​
    ​Details 
  • 2008 | Journal Article | 
    ​ ​Conditional Variable Importance for Random Forests​
    Strobl, C.; Boulesteix, A.-L.; Kneib, T. ; Augustin, T. & Zeileis, A.​ (2008) 
    BMC Bioinformatics, 9(1) art. 307​.​ DOI: https://doi.org/10.1186/1471-2105-9-307 
    ​Details  DOI 

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