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 | 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
    ​ ​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 | 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
    ​ ​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 
  • 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 
  • 2019 | Journal Article
    ​ ​Assessing the relationship between markers of glycemic control through flexible copula regression models​
    Espasandín-Domínguez, J.; Cadarso-Suárez, C.; Kneib, T. ; Marra, G.; Klein, N.; Radice, R. & Lado-Baleato, O. et al.​ (2019) 
    Statistics in Medicine, 38(27) pp. 5161​-5181​.​ DOI: https://doi.org/10.1002/sim.8358 
    ​Details  DOI  PMID  PMC 
  • 2019 | Journal Article
    ​ ​Lost in Translation: On the Problem of Data Coding in Penalized Whole Genome Regression with Interactions​
    Martini, J. W R; Rosales, F.; Ha, N.-T.; Heise, J.; Wimmer, V. & Kneib, T. ​ (2019) 
    G3: Genes, Genomes, Genetics, 9(4) pp. 1117​-1129​.​ DOI: https://doi.org/10.1534/g3.118.200961 
    ​Details  DOI  PMID  PMC 
  • 2019 | Journal Article
    ​ ​Mixed binary-continuous copula regression models with application to adverse birth outcomes​
    Klein, N. ; Kneib, T. ; Marra, G.; Radice, R.; Rokicki, S. & McGovern, M. E.​ (2019) 
    Statistics in Medicine, 38(3) pp. 413​-436​.​ DOI: https://doi.org/10.1002/sim.7985 
    ​Details  DOI  PMID  PMC 
  • 2018 | Journal Article
    ​ ​A primer on Bayesian distributional regression​
    Umlauf, N. & Kneib, T. ​ (2018) 
    Statistical Modelling, 18(3-4) pp. 219​-247​.​ DOI: https://doi.org/10.1177/1471082X18759140 
    ​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 
  • 2018 | Journal Article
    ​ ​Reconsidering the income-health relationship using distributional regression​
    Silbersdorff, A. ; Lynch, J.; Klasen, S. & Kneib, T. ​ (2018) 
    Health Economics, 27(7) pp. 1074​-1088​.​ DOI: https://doi.org/10.1002/hec.3656 
    ​Details  DOI  PMID  PMC 
  • 2017 | Journal Article
    ​ ​Bayesian regularisation in geoadditive expectile regression​
    Waldmann, E. ; Sobotka, F. & Kneib, T. ​ (2017) 
    Statistics and Computing, 27(6) pp. 1539​-1553​.​ DOI: https://doi.org/10.1007/s11222-016-9703-9 
    ​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 | 
    ​ ​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
    ​ ​Studying the relationship between a woman's reproductive lifespan and age at menarche using a Bayesian multivariate structured additive distributional regression model​
    Duarte, E.; de Sousa, B.; Cadarso-Suárez, C.; Klein, N. ; Kneib, T.   & Rodrigues, V.​ (2017) 
    Biometrical journal. Biometrische Zeitschrift, 59(6) pp. 1232​-1246​.​ DOI: https://doi.org/10.1002/bimj.201600245 
    ​Details  DOI  PMID  PMC 
  • 2017 | Journal Article
    ​ ​Predicting the occurrence of wildfires with binary structured additive regression models​
    Ríos-Pena, L.; Kneib, T. ; Cadarso-Suárez, C. & Marey-Pérez, M.​ (2017) 
    Journal of Environmental Management, 187 pp. 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)
    ​ ​Editorial "Joint modeling of longitudinal and time-to-event data and beyond"​
    Cadarso Suárez, C.; Klein, N.; Kneib, T. ; Molenberghs, G. & Rizopoulos, D.​ (2017) 
    Biometrical Journal, 59(6) pp. 1101​-1103​.​ DOI: https://doi.org/10.1002/bimj.201700180 
    ​Details  DOI  PMID  PMC 
  • 2017 | Journal Article
    ​ ​Structured additive distributional regression for analysing landings per unit effort in fisheries research​
    Mamouridis, V.; Klein, N. ; Kneib, T. ; Cadarso Suarez, C. & Maynou, F.​ (2017) 
    Mathematical Biosciences, 283 pp. 145​-154​.​ DOI: https://doi.org/10.1016/j.mbs.2016.11.016 
    ​Details  DOI  PMID  PMC 
  • 2017 | Journal Article
    ​ ​Boosting joint models for longitudinal and time-to-event data​
    Taylor-Robinson, D.; Pressler, T.; Schmid, M.; Mayr, A.; Waldmann, E. ; Klein, N.   & Kneib, T. ​ (2017) 
    Biometrical Journal, 59(6) pp. 1104-1121​-1121​.​ DOI: https://doi.org/10.1002/bimj.201600158 
    ​Details  DOI  PMID  PMC 
  • 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
    ​ ​Analysing farmland rental rates using Bayesian geoadditive quantile regression​
    März, A.; Klein, N. ; Kneib, T.   & Mußhoff, O. ​ (2016) 
    European Review of Agricultural Economics, 43(4) pp. 663​-698​.​ DOI: https://doi.org/10.1093/erae/jbv028 
    ​Details  DOI 
  • 2016 | Journal Article
    ​ ​Source estimation for propagation processes on complex networks with an application to delays in public transportation systems​
    Manitz, J. ; Harbering, J. ; Schmidt, M.; Kneib, T.   & Schoebel, A. ​ (2016) 
    Journal of the Royal Statistical Society. Series C, Applied statistics, 66(3) pp. 521​-536​.​ DOI: https://doi.org/10.1111/rssc.12176 
    ​Details  DOI 
  • 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 
  • 2016 | Journal Article
    ​ ​Structured fusion lasso penalized multi-state models​
    Sennhenn-Reulen, H.   & Kneib, T. ​ (2016) 
    Statistics in Medicine, 35(25) pp. 4637​-4659​.​ DOI: https://doi.org/10.1002/sim.7017 
    ​Details  DOI  PMID  PMC 
  • 2015 | Journal Article
    ​ ​Bayesian structured additive distributional regression for multivariate responses​
    Klein, N. ; Kneib, T. ; Klasen, S.   & Lang, S.​ (2015) 
    Journal of the Royal Statistical Society. Series C, Applied statistics, 64(4) pp. 569​-591​.​ DOI: https://doi.org/10.1111/rssc.12090 
    ​Details  DOI 
  • 2015 | Journal Article
    ​ ​Variational approximations in geoadditive latent Gaussian regression: mean and quantile regression​
    Waldmann, E.   & Kneib, T. ​ (2015) 
    Statistics and Computing, 25(6) pp. 1247​-1263​.​ DOI: https://doi.org/10.1007/s11222-014-9480-2 
    ​Details  DOI 
  • 2015 | Journal Article
    ​ ​Expectile and quantile regression-David and Goliath?​
    Waltrup, L. S.; Sobotka, F.; Kneib, T.   & Kauermann, G.​ (2015) 
    Statistical Modelling, 15(5) pp. 433​-456​.​ DOI: https://doi.org/10.1177/1471082X14561155 
    ​Details  DOI  WoS 
  • 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
    ​ ​Nonlife ratemaking and risk management with Bayesian generalized additive models for location, scale, and shape​
    Klein, N. ; Denuit, M.; Lang, S. & Kneib, T. ​ (2014) 
    Insurance: Mathematics and Economics, 55 pp. 225​-249​.​ DOI: https://doi.org/10.1016/j.insmatheco.2014.02.001 
    ​Details  DOI 
  • 2014 | Journal Article
    ​ ​Origin Detection During Food-borne Disease Outbreaks - A Case Study of the 2011 EHEC/HUS Outbreak in Germany​
    Manitz, J. ; Kneib, T. ; Schlather, M.; Helbing, D. & Brockmann, D.​ (2014) 
    PLoS Currents,.​ DOI: https://doi.org/10.1371/currents.outbreaks.f3fdeb08c5b9de7c09ed9cbcef5f01f2 
    ​Details  DOI  PMID  PMC 
  • 2014 | Journal Article
    ​ ​Bayesian Generalized Additive Models for Location, Scale, and Shape for Zero-Inflated and Overdispersed Count Data​
    Klein, N. ; Kneib, T.   & Lang, S.​ (2014) 
    Journal of the American Statistical Association, 110(509) pp. 405​-419​.​ DOI: https://doi.org/10.1080/01621459.2014.912955 
    ​Details  DOI 
  • 2014 | Journal Article
    ​ ​Bayesian bivariate quantile regression​
    Waldmann, E.   & Kneib, T. ​ (2014) 
    Statistical Modelling, 15(4) pp. 326​-344​.​ DOI: https://doi.org/10.1177/1471082x14551247 
    ​Details  DOI 
  • 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 
  • 2014 | Journal Article
    ​ ​Discussion of "The Evolution of Boosting Algorithms" and "Extending Statistical Boosting"​
    Bühlmann, P.; Gertheiss, J. ; Hieke, S.; Kneib, T. ; Ma, S.; Schumacher, M. & Tutz, G. et al.​ (2014) 
    Methods of Information in Medicine, 53(6) pp. 436​-445​.​ DOI: https://doi.org/10.3414/13100122 
    ​Details  DOI  PMID  PMC  WoS 
  • 2014 | Journal Article
    ​ ​Sentinel lymph node dissection in more than 1200 prostate cancer cases: Rate and prediction of lymph node involvement depending on preoperative tumor characteristics​
    Winter, A.; Kneib, T. ; Henke, R.-P. & Wawroschek, F.​ (2014) 
    International Journal of Urology, 21(1) pp. 58​-63​.​ DOI: https://doi.org/10.1111/iju.12184 
    ​Details  DOI  PMID  PMC 
  • 2013 | Monograph
    ​ ​Regression: ​models, methods and applications​ ​(3. ed.) 
    Fahrmeir, L.; Kneib, T. ; Lang, S.& Marx, B.​ (2013)
    Berlin​: Springer. DOI: https://doi.org/10.1007/978-3-642-34333-9 
    ​Details  DOI 
  • 2013 | Journal Article
    ​ ​Model building in nonproportional hazard regression​
    Rodríguez-Girondo, M.; Kneib, T. ; Cadarso-Suárez, C. & Abu-Assi, E.​ (2013) 
    Statistics in Medicine, 32(30) pp. 5301​-5314​.​ DOI: https://doi.org/10.1002/sim.5961 
    ​Details  DOI 
  • 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 | 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 
  • 2012 | Journal Article
    ​ ​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​
    Winter, A.; Kneib, T. ; Schatke, N.; Rohde, M.; Henke, R.-P. & Wawroschek, F.​ (2012) 
    Journal of Urology, 187(4S).​ DOI: https://doi.org/10.1016/j.juro.2012.02.233 
    ​Details  DOI 
  • 2012 | Journal Article
    ​ ​The effect of bark beetle infestation and salvage logging on bat activity in a national park​
    Mehr, M.; Brandl, R.; Kneib, T.   & Müller, J.​ (2012) 
    Biodiversity and Conservation, 21(11) pp. 2775​-2786​.​ DOI: https://doi.org/10.1007/s10531-012-0334-y 
    ​Details  DOI 
  • 2012 | Journal Article
    ​ ​Additive mixed models with Dirichlet process mixture and P-spline priors​
    Heinzl, F.; Fahrmeir, L. & Kneib, T. ​ (2012) 
    AStA Advances in Statistical Analysis, 96(1) pp. 47​-68​.​ DOI: https://doi.org/10.1007/s10182-011-0161-6 
    ​Details  DOI 
  • 2012 | Journal Article
    ​ ​Generalized additive models for location, scale and shape for high dimensional data-a flexible approach based on boosting​
    Mayr, A.; Fenske, N.; Hofner, B.; Kneib, T.   & Schmid, M.​ (2012) 
    Journal of the Royal Statistical Society. Series C, Applied statistics, 61(3) pp. 403​-427​.​ DOI: https://doi.org/10.1111/j.1467-9876.2011.01033.x 
    ​Details  DOI 
  • 2011 | Journal Article
    ​ ​Building Cox-type structured hazard regression models with time-varying effects​
    Hofner, B.; Kneib, T. ; Hartl, W. & Küchenhoff, H.​ (2011) 
    Statistical Modelling, 11(1) pp. 3​-24​.​ DOI: https://doi.org/10.1177/1471082x1001100102 
    ​Details  DOI 
  • 2011 | Journal Article
    ​ ​Estimating habitat suitability and potential population size for brown bears in the Eastern Alps​
    Güthlin, D.; Knauer, F.; Kneib, T. ; Küchenhoff, H.; Kaczensky, P.; Rauer, G. & Jonozovič, M. et al.​ (2011) 
    Biological Conservation, 144(5) pp. 1733​-1741​.​ DOI: https://doi.org/10.1016/j.biocon.2011.03.010 
    ​Details  DOI 
  • 2011 | Journal Article
    ​ ​Categorical structured additive regression for assessing habitat suitability in the spatial distribution of mussel seed abundance​
    Pata, M. P.; Kneib, T. ; Cadarso-Suarez, C.; Lustres-Pérez, V. & Fernández-Pulpeiro, E.​ (2011) 
    Environmetrics, 23(1) pp. 75​-84​.​ DOI: https://doi.org/10.1002/env.1140 
    ​Details  DOI 
  • 2010 | Journal Article
    ​ ​Flexible hazard ratio curves for continuous predictors in multi-state models​
    Cadarso-Suárez, C.; Meira-Machado, L.; Kneib, T.   & Gude, F.​ (2010) 
    Statistical Modelling, 10(3) pp. 291​-314​.​ DOI: https://doi.org/10.1177/1471082X0801000303 
    ​Details  DOI 
  • 2010 | Journal Article
    ​ ​Short-term influence of elevation of plasma homocysteine levels on cognitive function in young healthy adults​
    Alexopoulos, P.; Lehrl, S.; Richter-Schmidinger, T.; Kreusslein, A.; Hauenstein, T.; Bayerl, F. & Jung, P. et al.​ (2010) 
    The Journal of Nutrition, Health & Aging, 14(4) pp. 283​-287​.​ DOI: https://doi.org/10.1007/s12603-010-0062-5 
    ​Details  DOI 
  • 2010 | Journal Article
    ​ ​Flexible hazard ratio curves for continuous predictors in multi-state models: an application to breast cancer data​
    Cadarso-Suarez, C.; Meira-Machado, L.; Kneib, T.   & Gude, F.​ (2010) 
    Statistical Modelling, 10(3) pp. 291​-314​.​ DOI: https://doi.org/10.1177/1471082x0801000303 
    ​Details  DOI 
  • 2010 | Book Chapter
    ​ ​Generalized Semiparametric Regression Models with Nonparametric Effects of Covariates Measured with Error​
    Kneib, T. ; Brezger, A.& Crainiceanu, C. M.​ (2010)
    In:​Kneib, Thomas; Tutz, Gerhard​ (Eds.), Statistical Modelling and Regression Structures - Festschrift in the Honour of Ludwig Fahrmeir. ​Berlin: ​Springer.
    ​Details 
  • 2010 | Journal Article
    ​ ​Geoadditive expectile regression​
    Sobotka, F. & Kneib, T. ​ (2010) 
    Computational Statistics & Data Analysis, 56(4) pp. 755​-767​.​ DOI: https://doi.org/10.1016/j.csda.2010.11.015 
    ​Details  DOI 
  • 2010 | Anthology
    ​ ​Statistical Modelling and Regression Structures: ​Festschrift in Honour of Ludwig Fahrmeir​ ​
    Kneib, T.  & Tutz, G.​ (Eds.) (2010)
    Heidelberg: ​Physica-Verlag HD. DOI: https://doi.org/10.1007/978-3-7908-2413-1 
    ​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 
  • 2009 | Journal Article
    ​ ​Nosocomial Infection, Length of Stay, and Time-Dependent Bias​
    Beyersmann, J.; Kneib, T. ; Schumacher, M. & Gastmeier, P.​ (2009) 
    Infection Control & Hospital Epidemiology, 30(3) pp. 273​-276​.​ DOI: https://doi.org/10.1086/596020 
    ​Details  DOI 
  • 2009 | Journal Article
    ​ ​A new strategy to analyze possible association structures between dynamic nocturnal hormone activities and sleep alterations in humans​
    Kalus, S.; Kneib, T. ; Steiger, A.; Holsboer, F. & Yassouridis, A.​ (2009) 
    AJP: Regulatory, Integrative and Comparative Physiology, 296(4) pp. R1216​-R1227​.​ DOI: https://doi.org/10.1152/ajpregu.90530.2008 
    ​Details  DOI  PMID  PMC 
  • 2008 | Journal Article
    ​ ​Activity-guided antithrombin III therapy in severe surgical sepsis: efficacy and safety according to a retrospective data analysis​
    Moubarak, P.; Zilker, S.; Wolf, H.; Hofner, B.; Kneib, T. ; Küchenhoff, H. & Jauch, K.-W. et al.​ (2008) 
    Shock, 30(6) pp. 634​-641​.​ DOI: https://doi.org/10.1097/SHK.0b013e31817d3e14 
    ​Details  DOI  PMID  PMC 
  • 2008 | Journal Article
    ​ ​Bayesian semi parametric multi-state models​
    Kneib, T.   & Hennerfeind, A.​ (2008) 
    Statistical Modelling, 8(2) pp. 169​-198​.​ DOI: https://doi.org/10.1177/1471082x0800800203 
    ​Details  DOI 
  • 2008 | Journal Article
    ​ ​Saproxylic beetle assemblages related to silvicultural management intensity and stand structures in a beech forest in Southern Germany​
    Müller, J.; Bußler, H. & Kneib, T. ​ (2008) 
    Journal of Insect Conservation, 12(2) pp. 107​-124​.​ DOI: https://doi.org/10.1007/s10841-006-9065-2 
    ​Details  DOI 
  • 2008 | Journal Article
    ​ ​Propriety of posteriors in structured additive regression models: Theory and empirical evidence​
    Fahrmeir, L. & Kneib, T. ​ (2008) 
    Journal of Statistical Planning and Inference, 139(3) pp. 843​-859​.​ DOI: https://doi.org/10.1016/j.jspi.2008.05.036 
    ​Details  DOI 
  • 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 
  • 2008 | Journal Article
    ​ ​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​
    Weidinger, S.; Baurecht, H.; Wagenpfeil, S.; Henderson, J.; Novak, N.; Sandilands, A. & Chen, H. et al.​ (2008) 
    Journal of Allergy and Clinical Immunology, 122(3) pp. 560​-568​.​ DOI: https://doi.org/10.1016/j.jaci.2008.05.050 
    ​Details  DOI  PMID  PMC 
  • 2007 | Journal Article
    ​ ​Introduction to the Special Volume on "Ecology and Ecological Modelling in R"​
    Kneib, T.   & Petzoldt, T.​ (2007) 
    Journal of Statistical Software, 22(1).​ DOI: https://doi.org/10.18637/jss.v022.i01 
    ​Details  DOI 
  • 2006 | Thesis | Bachelor Thesis
    ​ ​Mixed model based inference in structured additive regression​
    Kneib, T. ​ (2006)
    Dr. Hut-Verlag.
    ​Details 
  • 2004 | Report
    ​ ​Bayesian semiparametric regression based on mixed model methodology: A tutorial​
    Kneib, T. ; Lang, S.& Brezger, A.​ (2004)
    ​Details 
  • 2004 | Working Paper
    ​ ​Bayesian semiparametric regression based on MCMC techniques: A tutorial​
    Kneib, T. ; Lang, S.& Brezger, A.​ (2004)
    Department of Statistics, University of Munich.
    ​Details 

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