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 
  • 2019 | Preprint
    ​ ​Noncrossing structured additive multiple-output Bayesian quantile regression models​
    Santos, B.& Kneib, T. ​ (2019)
    ​Details 
  • 2019 | Preprint
    ​ ​Generalised Joint Regression for Count Data with a Focus on Modelling Football Matches​
    van der Wurp, H.; Groll, A. H. ; Kneib, T.  & Marra, G.​ (2019)
    ​Details 
  • 2019 | Preprint
    ​ ​Operational risk, uncertainty, and the economy: a smooth transition extreme value approach​
    Hambuckers, J.& Kneib, T. ​ (2019)
    ​Details 
  • 2019 | Journal Article
    ​ ​Candidate-gene association analysis for a continuous phenotype with a spike at zero using parent-offspring trios​
    Klein, N. ; Entwistle, A.; Rosenberger, A. ; Kneib, T.   & Bickeböller, H. ​ (2019) 
    Journal of Applied Statistics, pp. 1​-15​.​ DOI: https://doi.org/10.1080/02664763.2019.1704226 
    ​Details  DOI 
  • 2019 | Book Chapter
    ​ ​Mehr als Durchschnittsstatistik: ​Eine kritische Einführung in Regressionsmethoden jenseits des Mittelwertes​
    Hohberg, M. ; Silbersdorff, A.  & Kneib, T. ​ (2019)
    In: Perspektiven einer pluralen Ökonomik pp. 231​-255.  DOI: https://doi.org/10.1007/978-3-658-16145-3_10 
    ​Details  DOI 
  • 2019 | Journal Article
    ​ ​LASSO-type penalization in the framework of generalized additive models for location, scale and shape​
    Groll, A.; Hambuckers, J.; Kneib, T.   & Umlauf, N.​ (2019) 
    Computational Statistics & Data Analysis, 140 pp. 59​-73​.​ DOI: https://doi.org/10.1016/j.csda.2019.06.005 
    ​Details  DOI 
  • 2019 | Journal Article
    ​ ​Multivariate effect priors in bivariate semiparametric recursive Gaussian models​
    Thaden, H.; Klein, N. & Kneib, T. ​ (2019) 
    Computational Statistics & Data Analysis, 137 pp. 51​-66​.​ DOI: https://doi.org/10.1016/j.csda.2018.12.004 
    ​Details  DOI 
  • 2019 | Journal Article
    ​ ​Modular regression - a Lego system for building structured additive distributional regression models with tensor product interactions​
    Kneib, T. ; Klein, N.; Lang, S. & Umlauf, N.​ (2019) 
    Test: an official journal of the Spanish Society of Statistics and Operations Research, 28(1) pp. 1​-39​.​ DOI: https://doi.org/10.1007/s11749-019-00631-z 
    ​Details  DOI 
  • 2019 | Journal Article
    ​ ​Rejoinder on: Modular regression - a Lego system for building structured additive distributional regression models with tensor product interactions​
    Kneib, T. ; Klein, N.; Lang, S. & Umlauf, N.​ (2019) 
    Test: an official journal of the Spanish Society of Statistics and Operations Research, 28(1) pp. 55​-59​.​ DOI: https://doi.org/10.1007/s11749-019-00636-8 
    ​Details  DOI 
  • 2019 | Journal Article | Erratum
    ​ ​Correction to: On the behaviour of marginal and conditional AIC in linear mixed models​
    Greven, S. & Kneib, T. ​ (2019) 
    Biometrika, art. asz051​.​ DOI: https://doi.org/10.1093/biomet/asz051 
    ​Details  DOI 
  • 2019 | Preprint
    ​ ​Using the Softplus Function to Construct Alternative Link Functions in Generalized Linear Models and Beyond​
    Wiemann, P.& Kneib, T. ​ (2019)
    ​Details 
  • 2019 | Preprint
    ​ ​Beyond unidimensional poverty analysis using distributional copula models for mixed ordered-continuous outcomes​
    Hohberg, M. ; Donat, F.; Marra, G.& Kneib, T. ​ (2019)
    ​Details 
  • 2019 | Preprint
    ​ ​Bayesian Gaussian distributional regression models for more efficient norm estimation​
    Voncken, L.; Kneib, T. ; Albers, C. J; Umlauf, N.& Timmerman, M.​ (2019). DOI: https://doi.org/10.31234/osf.io/7j8ym 
    ​Details  DOI 
  • 2019 | Journal Article
    ​ ​A trivariate additive regression model with arbitrary link functions and varying correlation matrix​
    Filippou, P.; Kneib, T. ; Marra, G. & Radice, R.​ (2019) 
    Journal of Statistical Planning and Inference, 199 pp. 236​-248​.​ DOI: https://doi.org/10.1016/j.jspi.2018.07.002 
    ​Details  DOI 
  • 2019 | Preprint
    ​ ​A multi-locus genetic risk score modulates social buffering of HPA axis activity in wild male primates​
    Gutleb, D. R.; Roos, C.; Heistermann, M.; De Moor, D.; Kneib, T. ; Noll, A.& Schülke, O. et al.​ (2019)
    ​Details 
  • 2019 | Journal Article
    ​ ​Directional bivariate quantiles: a robust approach based on the cumulative distribution function​
    Klein, N. & Kneib, T. ​ (2019) 
    Advances in Statistical Analysis,.​ DOI: https://doi.org/10.1007/s10182-019-00355-3 
    ​Details  DOI 
  • 2019 | Preprint
    ​ ​distreg.vis: Interactively visualizing distributional regression models​
    Stadlmann, S.& Kneib, T. ​ (2019)
    ​Details 
  • 2019 | Journal Article
    ​ ​Bayesian measurement error correction in structured additive distributional regression with an application to the analysis of sensor data on soil–plant variability​
    Pollice, A.; Jona Lasinio, G.; Rossi, R.; Amato, M.; Kneib, T.   & Lang, S.​ (2019) 
    Stochastic Environmental Research and Risk Assessment, 33(3) pp. 747​-763​.​ DOI: https://doi.org/10.1007/s00477-019-01667-1 
    ​Details  DOI 
  • 2019 | Journal Article
    ​ ​Adaptive semiparametric M-quantile regression​
    Otto-Sobotka, F.; Salvati, N.; Ranalli, M. G. & Kneib, T. ​ (2019) 
    Econometrics and Statistics, 11 pp. 116​-129​.​ DOI: https://doi.org/10.1016/j.ecosta.2019.03.001 
    ​Details  DOI 
  • 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 | Preprint
    ​ ​Generalized additive models for location, scale and shape for program evaluation: ​A guide to practice​
    Hohberg, M. ; Pütz, P.  & Kneib, T. ​ (2018)
    ​Details 
  • 2018 | Preprint
    ​ ​Conditional Model Selection in Mixed-Effects Models with cAIC4​
    Säfken, B.; Rügamer, D.; Kneib, T.  & Greven, S.​ (2018)
    ​Details  arXiv 
  • 2018 | Preprint
    ​ ​Understanding the Economic Determinants of the Severity of Operational Losses: A Regularized Generalized Pareto Regression Approach​
    Hambuckers, J.; Groll, A.& Kneib, T. ​ (2018)
    ​Details 
  • 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
    ​ ​Understanding the economic determinants of the severity of operational losses: A regularized generalized Pareto regression approach​
    Hambuckers, J.; Groll, A. & Kneib, T. ​ (2018) 
    Journal of Applied Econometrics, 33(6) pp. 898​-935​.​ DOI: https://doi.org/10.1002/jae.2638 
    ​Details  DOI 
  • 2018 | Journal Article | Editorial Contribution (Editorial, Introduction, Epilogue)
    ​ ​Editorial 'Bridging the gap between methodology and applications: Tutorials on semiparametric regression'​
    Groll, A.; Kneib, T.   & Mayr, A.​ (2018) 
    Statistical Modelling, 18(3-4) pp. 199​-202​.​ DOI: https://doi.org/10.1177/1471082X18761252 
    ​Details  DOI 
  • 2018 | Journal Article | Editorial Contribution (Editorial, Introduction, Epilogue)
    ​ ​Editorial: Special issue on quantile regression and semiparametric methods​
    He, X.; Kneib, T. ; Lamarche, C. & Wang, L.​ (2018) 
    Econometrics and Statistics, 8 pp. 1​-2​.​ DOI: https://doi.org/10.1016/j.ecosta.2018.09.002 
    ​Details  DOI 
  • 2018 | Journal Article
    ​ ​On the dependency of soccer scores – a sparse bivariate Poisson model for the UEFA European football championship 2016​
    Groll, A.; Kneib, T. ; Mayr, A. & Schauberger, G.​ (2018) 
    Journal of Quantitative Analysis in Sports, 14(2) pp. 65​-79​.​ DOI: https://doi.org/10.1515/jqas-2017-0067 
    ​Details  DOI 
  • 2018 | Preprint
    ​ ​A Behavioral Economic Perspective on Demand Responsive Transportation​
    Herbst, H.; Minnich, A.; Herminghaus, S.; Kneib, T. ; Wacker, B.& Schlüter, J. C.​ (2018)
    ​Details 
  • 2018 | Journal Article
    ​ ​Studying the occurrence and burnt area of wildfires using zero-one-inflated structured additive beta regression​
    Ríos-Pena, L.; Kneib, T. ; Cadarso-Suárez, C.; Klein, N. & Marey-Pérez, M.​ (2018) 
    Environmental Modelling & Software, 110 pp. 107​-118​.​ DOI: https://doi.org/10.1016/j.envsoft.2018.03.008 
    ​Details  DOI 
  • 2018 | Journal Article
    ​ ​Bayesian Multivariate Distributional Regression With Skewed Responses and Skewed Random Effects​
    Michaelis, P.; Klein, N. & Kneib, T. ​ (2018) 
    Journal of Computational and Graphical Statistics, 27(3) pp. 602​-611​.​ DOI: https://doi.org/10.1080/10618600.2017.1395343 
    ​Details  DOI 
  • 2018 | Journal Article
    ​ ​Flexible estimation of time-varying effects for frequently purchased retail goods: a modeling approach based on household panel data​
    Baumgartner, B.; Guhl, D.; Kneib, T.   & Steiner, W. J.​ (2018) 
    OR Spectrum, 40(4) pp. 837​-873​.​ DOI: https://doi.org/10.1007/s00291-018-0530-6 
    ​Details  DOI 
  • 2018 | Journal Article
    ​ ​Structural Equation Models for Dealing With Spatial Confounding​
    Thaden, H. & Kneib, T. ​ (2018) 
    The American Statistician, 72(3) pp. 239​-252​.​ DOI: https://doi.org/10.1080/00031305.2017.1305290 
    ​Details  DOI 
  • 2018 | Journal Article
    ​ ​A Markov-switching generalized additive model for compound Poisson processes, with applications to operational loss models​
    Hambuckers, J.; Kneib, T. ; Langrock, R. & Silbersdorff, A. ​ (2018) 
    Quantitative Finance, 18(10) pp. 1679​-1698​.​ DOI: https://doi.org/10.1080/14697688.2017.1417625 
    ​Details  DOI 
  • 2018 | Journal Article
    ​ ​Estimating time-varying parameters in brand choice models: A semiparametric approach​
    Guhl, D.; Baumgartner, B.; Kneib, T.   & Steiner, W. J.​ (2018) 
    International Journal of Research in Marketing, 35(3) pp. 394​-414​.​ DOI: https://doi.org/10.1016/j.ijresmar.2018.03.003 
    ​Details  DOI 
  • 2018 | Journal Article
    ​ ​Geographical differences in blood potassium detected using a structured additive distributional regression model​
    Espasandín-Domínguez, J.; Benítez-Estévez, A. J.; Cadarso-Suárez, C.; Kneib, T. ; Barreiro-Martínez, T.; Casas-Méndez, B. & Gude, F.​ (2018) 
    Spatial Statistics, 24 pp. 1​-13​.​ DOI: https://doi.org/10.1016/j.spasta.2018.03.001 
    ​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 | Preprint
    ​ ​A Markov-Switching Generalized Additive Model for Compound Poisson Processes, with Applications to Operational Losses Models​
    Hambuckers, J.; Kneib, T. ; Langrock, R.& Silbersdorff, A. ​ (2017)
    ​Details 
  • 2017 | Preprint
    ​ ​Gradient boosting in Markov-switching generalized additive models for location, scale and shape​
    Adam, T.; Mayr, A.& Kneib, T. ​ (2017)
    ​Details  arXiv 
  • 2017 | Preprint
    ​ ​Determinants of the Variability of Oxygen Saturation during the First Minutes of Life of Term Neonates​
    Mascarenhas, A.; Marques, F.; Silva, S.; Gouveia, S.; Alves, M.; Virella, D.& Papoila, A. L. et al.​ (2017)
    ​Details 
  • 2017 | Journal Article
    ​ ​Generalized additive models with flexible response functions​
    Spiegel, E.; Kneib, T.   & Otto-Sobotka, F.​ (2017) 
    Statistics and Computing, 29(1) pp. 123​-138​.​ DOI: https://doi.org/10.1007/s11222-017-9799-6 
    ​Details  DOI 
  • 2017 | Journal Article
    ​ ​Integrating multivariate conditionally autoregressive spatial priors into recursive bivariate models for analyzing environmental sensitivity of mussels​
    Thaden, H. ; Pata, M. P.; Klein, N. ; Cadarso-Suárez, C. & Kneib, T. ​ (2017) 
    Spatial Statistics, 22 pp. 419​-433​.​ DOI: https://doi.org/10.1016/j.spasta.2017.07.005 
    ​Details  DOI 
  • 2017 | Journal Article
    ​ ​Exploring risk factors in breast cancer screening program data using structured geoadditive models with high order interaction​
    Duarte, E.; de Sousa, B.; Cadarso-Suárez, C.; Kneib, T.   & Rodrigues, V.​ (2017) 
    Spatial Statistics, 22 pp. 403​-418​.​ DOI: https://doi.org/10.1016/j.spasta.2017.07.004 
    ​Details  DOI 
  • 2017 | Journal Article
    ​ ​Comparing canopy leaf temperature of three Central European tree species based on simultaneous confidence bands for penalized splines​
    Vonrüti, M.; Spasojevic, A.; Nölke, N. ; Kneib, T.   & Kleinn, C. ​ (2017) 
    Environmental and Ecological Statistics, 24(3) pp. 385​-398​.​ DOI: https://doi.org/10.1007/s10651-017-0375-1 
    ​Details  DOI 
  • 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
    ​ ​A penalized spline estimator for fixed effects panel data models​
    Pütz, P. & Kneib, T. ​ (2017) 
    Advances in Statistical Analysis, 102(2) pp. 145​-166​.​ DOI: https://doi.org/10.1007/s10182-017-0296-1 
    ​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
    ​ ​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
    ​ ​Smoothing Parameter and Model Selection for General Smooth Models Comment​
    Kneib, T. ​ (2016) 
    Journal of the American Statistical Association, 111(516) pp. 1563​-1565​.​ DOI: https://doi.org/10.1080/01621459.2016.1250576 
    ​Details  DOI  WoS 
  • 2016 | Journal Article | Research Paper
    ​ ​A Semiparametric Analysis of Conditional Income Distributions​
    Sohn, A. ; Klein, N.   & Kneib, T. ​ (2016) 
    Schmollers Jahrbuch, 135(1) pp. 13​-22​.​ DOI: https://doi.org/10.3790/schm.135.1.13 
    ​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
    ​ ​Maximum penalized likelihood estimation in semiparametric mark-recapture-recovery models​
    Michelot, T.; Langrock, R. ; Kneib, T.   & King, R.​ (2015) 
    Biometrical Journal, 58(1) pp. 222​-239​.​ DOI: https://doi.org/10.1002/bimj.201400222 
    ​Details  DOI 
  • 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 | Review
    ​ ​Applied Statistical Inference: Likelihood and Bayes. L.Held and D.Sabanés Bové (2014). Heidelberg: Springer. 376 pages, ISBN: 3642378862​
    Kneib, T. ​ (2015)
    Biometrical Journal, 57​(2) pp. 362​-363​.​ DOI: https://doi.org/10.1002/bimj.201400209 
    ​Details  DOI 
  • 2015 | Journal Article
    ​ ​Nonparametric inference in hidden Markov models using P-splines​
    Langrock, R. ; Kneib, T. ; Sohn, A.   & Ruiter, S. L. de​ (2015) 
    Biometrics, 71(2) pp. 520​-528​.​ DOI: https://doi.org/10.1111/biom.12282 
    ​Details  DOI 
  • 2015 | Journal Article
    ​ ​Scale-Dependent Priors for Variance Parameters in Structured Additive Distributional Regression​
    Klein, N.   & Kneib, T. ​ (2015) 
    Bayesian Analysis, 11(4) pp. 1071​-1106​.​ DOI: https://doi.org/10.1214/15-ba983 
    ​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 | Preprint
    ​ ​Statistical risk analysis for real estate collateral valuation using Bayesian distributional and quantile regression​
    Razen, A.; Brunauer, W.; Klein, N.; Kneib, T. ; Lang, S.& Umlauf, N.​ (2015)
    ​Details 
  • 2015 | Preprint
    ​ ​Intergenerational social mobility in the United States: A multivariate analysis using Bayesian distributional regression​
    März, A.; Klein, N.; Kneib, T.  & Mußhoff, O.​ (2015)
    ​Details 
  • 2015 | Journal Article
    ​ ​Bayesian structured additive distributional regression with an application to regional income inequality in Germany​
    Klein, N. ; Kneib, T. ; Lang, S. & Sohn, A. ​ (2015) 
    The Annals of Applied Statistics, 9(2) pp. 1024​-1052​.​ DOI: https://doi.org/10.1214/15-aoas823 
    ​Details  DOI 
  • 2015 | Journal Article
    ​ ​Simultaneous inference in structured additive conditional copula regression models: a unifying Bayesian approach​
    Klein, N.   & Kneib, T. ​ (2015) 
    Statistics and Computing, 26(4) pp. 841​-860​.​ DOI: https://doi.org/10.1007/s11222-015-9573-6 
    ​Details  DOI 
  • 2015 | Journal Article
    ​ ​BayesX: Analyzing Bayesian Structured Additive Regression Models​
    Brezger, A.; Kneib, T.   & Lang, S.​ (2015) 
    Journal of Statistical Software, 14(11).​ DOI: https://doi.org/10.18637/jss.v014.i11 
    ​Details  DOI 
  • 2015 | Journal Article
    ​ ​Boosting multi-state models​
    Reulen, H. & Kneib, T. ​ (2015) 
    Lifetime Data Analysis, 22(2) pp. 241​-262​.​ DOI: https://doi.org/10.1007/s10985-015-9329-9 
    ​Details  DOI 
  • 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 | Preprint
    ​ ​Model Choice in Cox-Type Additive Hazard Regression Models with Time-Varying Effects​
    Hofner, B.; Kneib, T. ; Hartl, W. H.& Kuchenho, H.​ (2014)
    ​Details 
  • 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 | Conference Paper
    ​ ​Semiparametric ROC Regression based on Conditional Transformation Models​
    Rodríguez-Álvarez, M. X.; Kneib, T.   & Cadarso-Suárez, C.​ (2014)
    ​Proceedings of the 29th Internacional Workshop on Statistical Modelling pp. 145​-148. (Vol. 2). ​29th Internacional Workshop on Statistical Modelling​, Göttingen.
    ​Details 
  • 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
    ​ ​Structured additive regression modeling of age of menarche and menopause in a breast cancer screening program​
    Duarte, E.; Sousa, B. de; Cadarso-Suarez, C.; Rodrigues, V. & Kneib, T. ​ (2014) 
    Biometrical Journal, 56(3) pp. 416​-427​.​ DOI: https://doi.org/10.1002/bimj.201200260 
    ​Details  DOI 
  • 2014 | Journal Article
    ​ ​Bayesian Nonparametric Instrumental Variables Regression Based on Penalized Splines and Dirichlet Process Mixtures​
    Wiesenfarth, M. ; Hisgen, C. M.; Kneib, T.   & Cadarso-Suarez, C.​ (2014) 
    Journal of Business & Economic Statistics, 32(3) pp. 468​-482​.​ DOI: https://doi.org/10.1080/07350015.2014.907092 
    ​Details  DOI 
  • 2014 | Journal Article
    ​ ​Semiparametric stochastic volatility modelling using penalized splines​
    Langrock, R. ; Michelot, T.; Sohn, A.   & Kneib, T. ​ (2014) 
    Computational Statistics, 30(2) pp. 517​-537​.​ DOI: https://doi.org/10.1007/s00180-014-0547-5 
    ​Details  DOI 
  • 2014 | Journal Article
    ​ ​Bayesian accelerated failure time models based on penalized mixtures of Gaussians: regularization and variable selection​
    Konrath, S.; Fahrmeir, L. & Kneib, T. ​ (2014) 
    AStA Advances in Statistical Analysis, 99(3) pp. 259​-280​.​ DOI: https://doi.org/10.1007/s10182-014-0240-6 
    ​Details  DOI 
  • 2014 | Preprint
    ​ ​Semiparametric Mode Regression​
    Oelker, M. R.; Sobotka, F.; Klein, N.& Kneib, T. ​ (2014)
    ​Details 
  • 2014 | Report
    ​ ​A New Semiparametric Approach to Analysing Conditional Income Distributions​
    Sohn, A. ; Klein, N.& Kneib, T. ​ (2014). DOI: https://doi.org/10.2139/ssrn.2404335 
    ​Details  DOI 
  • 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
    ​ ​Fast smoothing parameter separation in multidimensional generalized P-splines: the SAP algorithm​
    Rodríguez-Álvarez, M. X.; Lee, D.-J.; Kneib, T. ; Durbán, M. & Eilers, P.​ (2014) 
    Statistics and Computing, 25(5) pp. 941​-957​.​ DOI: https://doi.org/10.1007/s11222-014-9464-2 
    ​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
    ​ ​A unified framework of constrained regression​
    Hofner, B.; Kneib, T.   & Hothorn, T.​ (2014) 
    Statistics and Computing, 26(1-2) pp. 1​-14​.​ DOI: https://doi.org/10.1007/s11222-014-9520-y 
    ​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
    ​ ​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 
  • 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 
  • 2013 | Preprint
    ​ ​Extended Additive Regression for Analysing LPUE Indices in Fishery Research​
    Mamouridis, V.; Klein, N. ; Kneib, T. ; Cadarso, C.& Maynou, F.​ (2013)
    ​Details 
  • 2013 | Preprint
    ​ ​Nonparametric inference in hidden Markov models using P-splines​
    Langrock, R.; Kneib, T. ; Sohn, A.  & DeRuiter, S.​ (2013)
    ​Details  arXiv 
  • 2013 | Preprint
    ​ ​Semiparametric stochastic volatility modelling using penalized splines​
    Langrock, R.; Michelot, T.; Sohn, A.  & Kneib, T. ​ (2013)
    ​Details  arXiv 
  • 2013 | Conference Paper
    ​ ​Fast algorithm for smoothing parameter selection in multidimensional P-splines​
    Rodríguez-Álvarez, M. X.; Kneib, T. ; Lee, D.-J. & Durbán, M.​ (2013)
    ​Proceedings of the 28th Internacional Workshop on Statistical Modelling pp. 343​-349. ​28th Internacional Workshop on Statistical Modelling​, Palermo, Italy.
    ​Details 
  • 2013 | Journal Article
    ​ ​Bivariate cumulative probit model for the comparison of neuronal encoding hypotheses​
    Hillmann, J.; Kneib, T. ; Koepcke, L.; Juárez Paz, L. M. & Kretzberg, J.​ (2013) 
    Biometrical Journal, 56(1) pp. 23​-43​.​ DOI: https://doi.org/10.1002/bimj.201200161 
    ​Details  DOI 
  • 2013 | Journal Article
    ​ ​Penalized likelihood and Bayesian function selection in regression models​
    Scheipl, F.; Kneib, T.   & Fahrmeir, L.​ (2013) 
    AStA Advances in Statistical Analysis, 97(4) pp. 349​-385​.​ DOI: https://doi.org/10.1007/s10182-013-0211-3 
    ​Details  DOI 
  • 2013 | Book Chapter
    ​ ​Quantile Regression​
    Fahrmeir, L.; Kneib, T. ; Lang, S.& Marx, B.​ (2013)
    In: Regression: Modelle, Methoden und Anwendungen pp. 597​-620. (3. ed.). ​Berlin: ​Springer. DOI: https://doi.org/10.1007/978-3-642-34333-9_10 
    ​Details  DOI 
  • 2013 | Book Chapter
    ​ ​The Classical Linear Model​
    Fahrmeir, L.; Kneib, T. ; Lang, S.& Marx, B.​ (2013)
    In: Regression: Modelle, Methoden und Anwendungen pp. 73​-175. (3. ed.). ​Berlin: ​Springer. DOI: https://doi.org/10.1007/978-3-642-34333-9_3 
    ​Details  DOI 
  • 2013 | Journal Article
    ​ ​Epidemiology of suicide in Spain, 1981–2008: A spatiotemporal analysis​
    Álvaro-Meca, A.; Kneib, T. ; Gil-Prieto, R. & Gil de Miguel, A.​ (2013) 
    Public Health, 127(4) pp. 380​-385​.​ DOI: https://doi.org/10.1016/j.puhe.2012.12.007 
    ​Details  DOI 
  • 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 | Book Chapter
    ​ ​Bayesian Smoothing, Shrinkage and Variable Selection in Hazard Regression​
    Konrath, S.; Fahrmeir, L.& Kneib, T. ​ (2013)
    In:​Becker, Claudia; Fried, Roland; Kuhnt, Sonja​ (Eds.), Robustness and Complex Data Structures: Festschrift in Honour of Ursula Gather pp. 149​-170. ​Berlin, Heidelberg: ​Springer. DOI: https://doi.org/10.1007/978-3-642-35494-6_10 
    ​Details  DOI 
  • 2013 | Journal Article
    ​ ​Estimating the relationship between women's education and fertility in Botswana by using an instrumental variable approach to semiparametric expectile regression​
    Sobotka, F.; Radice, R.; Marra, G. & Kneib, T. ​ (2013) 
    Journal of the Royal Statistical Society. Series C, Applied statistics, 62(1) pp. 25​-45​.​ DOI: https://doi.org/10.1111/j.1467-9876.2012.01050.x 
    ​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 | Book Chapter
    ​ ​Mixed Models​
    Fahrmeir, L.; Kneib, T. ; Lang, S.& Marx, B.​ (2013)
    In: Regression: Modelle, Methoden und Anwendungen pp. 349​-412. (3. ed.). ​Berlin: ​Springer. DOI: https://doi.org/10.1007/978-3-642-34333-9_7 
    ​Details  DOI 
  • 2013 | Book Chapter
    ​ ​Regression Models: Modelle, Methoden und Anwendungen​
    Fahrmeir, L.; Kneib, T. ; Lang, S.& Marx, B.​ (2013)
    In:​Fahrmeir, Ludwig; Kneib, Thomas; Lang, Stefan​ (Eds.), Regression: Modelle, Methoden und Anwendungen pp. 21​-72. ​Berlin, Heidelberg: ​Springer. DOI: https://doi.org/10.1007/978-3-642-34333-9_2 
    ​Details  DOI 
  • 2013 | Book Chapter
    ​ ​Bayesian Multilevel Models​
    Fahrmeir, L.; Kneib, T.  & Lang, S.​ (2013)
    In:​Scott, Marc A.; Simonoff, Jeffrey S.; Marx, Brian D.​ (Eds.), The SAGE Handbook of Multilevel Modeling pp. 53​-72. ​London: ​SAGE Publications Ltd. DOI: https://doi.org/10.4135/9781446247600.n4 
    ​Details  DOI 
  • 2013 | Journal Article
    ​ ​Conditional transformation models​
    Hothorn, T.; Kneib, T.   & Bühlmann, P.​ (2013) 
    Journal of the Royal Statistical Society: Series B (Statistical Methodology), 76(1) pp. 3​-27​.​ DOI: https://doi.org/10.1111/rssb.12017 
    ​Details  DOI 
  • 2013 | Preprint
    ​ ​Risk of hospital readmission and death among HIV infected patients with tuberculosis. A recurrent event analysis​
    Alvaro-Meca, A.; Reulen, H.; Kneib, T. ; Rodriguez-Gijon, L.; Gil de Miguel, A.& Resino, S.​ (2013)
    ​Details 
  • 2013 | Journal Article
    ​ ​Rejoinder​
    Kneib, T. ​ (2013) 
    Statistical Modelling, 13(4) pp. 373​-385​.​ DOI: https://doi.org/10.1177/1471082X13494531 
    ​Details  DOI  WoS 
  • 2013 | Book Chapter
    ​ ​Nonparametric Regression​
    Fahrmeir, L.; Kneib, T. ; Lang, S.& Marx, B.​ (2013)
    In: Regression: Modelle, Methoden und Anwendungen pp. 413​-533. (3. ed.). ​Berlin: ​Springer. DOI: https://doi.org/10.1007/978-3-642-34333-9_8 
    ​Details  DOI 
  • 2013 | Book Chapter
    ​ ​Generalized Linear Models​
    Fahrmeir, L.; Kneib, T. ; Lang, S.& Marx, B.​ (2013)
    In: Regression: Modelle, Methoden und Anwendungen pp. 269​-324. (3. ed.). ​Berlin: ​Springer. DOI: https://doi.org/10.1007/978-3-642-34333-9_5 
    ​Details  DOI 
  • 2013 | Book Chapter
    ​ ​Extensions of the Classical Linear Model​
    Fahrmeir, L.; Kneib, T. ; Lang, S.& Marx, B.​ (2013)
    In: Regression: Modelle, Methoden und Anwendungen pp. 177​-267. (3. ed.). ​Berlin: ​Springer. DOI: https://doi.org/10.1007/978-3-642-34333-9_4 
    ​Details  DOI 
  • 2013 | Book Chapter
    ​ ​Categorical Regression Models​
    Fahrmeir, L.; Kneib, T. ; Lang, S.& Marx, B.​ (2013)
    In: Regression: Modelle, Methoden und Anwendungen pp. 325​-347. (3. ed.). ​Berlin: ​Springer. DOI: https://doi.org/10.1007/978-3-642-34333-9_6 
    ​Details  DOI 
  • 2013 | Book Chapter
    ​ ​Structured Additive Regression​
    Fahrmeir, L.; Kneib, T. ; Lang, S.& Marx, B.​ (2013)
    In: Regression: Modelle, Methoden und Anwendungen pp. 535​-595. (3. ed.). ​Berlin: ​Springer. DOI: https://doi.org/10.1007/978-3-642-34333-9_9 
    ​Details  DOI 
  • 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 
  • 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
    ​ ​Spike-and-Slab Priors for Function Selection in Structured Additive Regression Models​
    Scheipl, F.; Fahrmeir, L. & Kneib, T. ​ (2012) 
    Journal of the American Statistical Association, 107(500) pp. 1518​-1532​.​ DOI: https://doi.org/10.1080/01621459.2012.737742 
    ​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
    ​ ​Impact of comorbidities and surgery on health related transitions in pancreatic cancer admissions: A multi state model​
    Álvaro-Meca, A.; Kneib, T. ; Prieto, R. G. & Miguel, Á. G. de​ (2012) 
    Cancer Epidemiology, 36(2) pp. e142​-e146​.​ DOI: https://doi.org/10.1016/j.canep.2011.12.005 
    ​Details  DOI 
  • 2012 | Journal Article
    ​ ​Novel Kernel for Correcting Significance Bias in the Logistic Kernel Machine Test with an Application to Rheumatoid Arthritis​
    Freytag, S.; Amos, C. I.; Bickeböller, H. ; Kneib, T.   & Schlather, M.​ (2012) 
    Human Heredity, 74 pp. 97​-108​.​
    ​Details 
  • 2012 | Preprint
    ​ ​expectreg: An R Package for Expectile Regression​
    Sobotka, F.; Schnabel, S.; Schulze Waltrup, L.; Kauermann, G.& Kneib, T. ​ (2012)
    ​Details 
  • 2012 | Conference Abstract
    ​ ​Novel Kernel Function in the Logistic Kernel Machine Test for Pathways in GWA Studies​
    Freytag, S.; Amos, C. I.; Bickeböller, H. ; Kneib, T.   & Schlather, M.​ (2012)
    Genetic Epidemiology, 36(7) ​Annual Meeting of the International-Genetic-Epidemiology-Society (IGES)​, Stevenson, WA.
    Hoboken​: Wiley-Blackwell.
    ​Details  WoS 
  • 2012 | Journal Article
    ​ ​Flexible Geoadditive Survival analysis of Non-Hodgkin Lymphoma in Peru​
    Flores, C.; Rodriguez-Girondo, M.; Cadarso-Suarez, C.; Kneib, T. ; Gomez, G. & Casanova, L.​ (2012) 
    SORT, 36 pp. 221​-230​.​
    ​Details 
  • 2012 | Journal Article
    ​ ​Discussion on "Analysing visual receptive fields through generalised additive models with interactions" by Rodríguez-Álvarez, M. X., Cadarso-Suárez, C. and González, F.​
    Kneib, T. ​ (2012) 
    SORT - Statistics and Operations Research Transactions, 36 pp. 37​-38​.​
    ​Details 
  • 2012 | Journal Article
    ​ ​Variable selection and model choice in structured survival models​
    Hofner, B.; Hothorn, T. & Kneib, T. ​ (2012) 
    Computational Statistics, 28(3) pp. 1079​-1101​.​ DOI: https://doi.org/10.1007/s00180-012-0337-x 
    ​Details  DOI 
  • 2012 | Journal Article
    ​ ​Multilevel structured additive regression​
    Lang, S.; Umlauf, N.; Wechselberger, P.; Harttgen, K.   & Kneib, T. ​ (2012) 
    Statistics and Computing, 24(2) pp. 223​-238​.​ DOI: https://doi.org/10.1007/s11222-012-9366-0 
    ​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 | Book Chapter
    ​ ​A Space-Time Study on Forest Health​
    Kneib, T.  & Fahrmeir, L.​ (2011)
    In:​Chandler, R. E.; Scott, M.​ (Eds.), Statistical Methods for Trend Detection and Analysis in the Environmental Sciences. ​Wiley. DOI: https://doi.org/10.1002/9781119991571 
    ​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
    ​ ​Differential decomposition of humic acids by marine and estuarine bacterial communities at varying salinities​
    Rocker, D.; Kisand, V.; Scholz-Böttcher, B.; Kneib, T. ; Lemke, A.; Rullkötter, J. & Simon, M.​ (2011) 
    Biogeochemistry, 111(1-3) pp. 331​-346​.​ DOI: https://doi.org/10.1007/s10533-011-9653-4 
    ​Details  DOI 
  • 2011 | Preprint
    ​ ​What to expect – an R vignette for expectreg​
    Otto-Sobotka, F.; Kneib, T. ; Schnabel, S.& Eilers, P.​ (2011)
    ​Details 
  • 2011 | Journal Article
    ​ ​Identifying Risk Factors for Severe Childhood Malnutrition by Boosting Additive Quantile Regression​
    Fenske, N.; Kneib, T.   & Hothorn, T.​ (2011) 
    Journal of the American Statistical Association, 106(494) pp. 494​-510​.​ DOI: https://doi.org/10.1198/jasa.2011.ap09272 
    ​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 
  • 2011 | Monograph
    ​ ​Bayesian Smoothing and Regression for Longitudinal, Spatial and Event History Data​ ​
    Fahrmeir, L.& Kneib, T. ​ (2011)
    Oxford University Press. DOI: https://doi.org/10.1093/acprof:oso/9780199533022.001.0001 
    ​Details  DOI 
  • 2011 | Journal Article
    ​ ​A Framework for Unbiased Model Selection Based on Boosting​
    Hofner, B.; Hothorn, T.; Kneib, T.   & Schmid, M.​ (2011) 
    Journal of Computational and Graphical Statistics, 20(4) pp. 956​-971​.​ DOI: https://doi.org/10.1198/jcgs.2011.09220 
    ​Details  DOI 
  • 2011 | Book Chapter
    ​ ​Spatial Smoothing, Interactions and Geoadditive Regression​
    Fahrmeir, L.& Kneib, T. ​ (2011)
    In: Bayesian Smoothing and Regression for Longitudinal, Spatial and Event History Data pp. 307​-414.  DOI: https://doi.org/10.1093/acprof:oso/9780199533022.003.0005 
    ​Details  DOI 
  • 2011 | Book Chapter
    ​ ​Event History Data​
    Fahrmeir, L.& Kneib, T. ​ (2011)
    In: Bayesian Smoothing and Regression for Longitudinal, Spatial and Event History Data pp. 415​-494.  DOI: https://doi.org/10.1093/acprof:oso/9780199533022.003.0006 
    ​Details  DOI 
  • 2011 | Book Chapter
    ​ ​Introduction: Scope of the Book and Applications​
    Fahrmeir, L.& Kneib, T. ​ (2011)
    In: Bayesian Smoothing and Regression for Longitudinal, Spatial and Event History Data pp. 1​-17.  DOI: https://doi.org/10.1093/acprof:oso/9780199533022.003.0001 
    ​Details  DOI 
  • 2011 | Book Chapter
    ​ ​Generalized Linear Mixed Models​
    Fahrmeir, L.& Kneib, T. ​ (2011)
    In: Bayesian Smoothing and Regression for Longitudinal, Spatial and Event History Data pp. 107​-177.  DOI: https://doi.org/10.1093/acprof:oso/9780199533022.003.0003 
    ​Details  DOI 
  • 2011 | Book Chapter
    ​ ​Basic Concepts for Smoothing and Semiparametric Regression​
    Fahrmeir, L.& Kneib, T. ​ (2011)
    In: Bayesian Smoothing and Regression for Longitudinal, Spatial and Event History Data pp. 18​-106.  DOI: https://doi.org/10.1093/acprof:oso/9780199533022.003.0002 
    ​Details  DOI 
  • 2011 | Book Chapter
    ​ ​Semiparametric Mixed Models for Longitudinal Data​
    Fahrmeir, L.& Kneib, T. ​ (2011)
    In: Bayesian Smoothing and Regression for Longitudinal, Spatial and Event History Data pp. 178​-306.  DOI: https://doi.org/10.1093/acprof:oso/9780199533022.003.0004 
    ​Details  DOI 
  • 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 | Report
    ​ ​GAMLSS for high-dimensional data – a flexible approach based on boosting​
    Mayr, A.; Hofner, B.; Kneib, T.  & Schmid, M.​ (2010)
    ​Details 
  • 2010 | Conference Paper
    ​ ​Categorical structured additive regression for the assessment of habitat suitability in the spatial distribution of mussel seed abundance​
    Pata, M. P.; Kneib, T. ; Cadarso-Suárez, C.; Lustres-Pérez, V. & Fernández-Pulpeiro, E.​ (2010)
    ​METMAV International Workshop on Spatio-Temporal Modelling​, Santiago de Compostela. DOI: https://doi.org/10.13140/2.1.3714.8806 
    ​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
    ​ ​High dimensional structured additive regression models: Bayesian regularization, smoothing and predictive performance​
    Kneib, T. ; Konrath, S. & Fahrmeir, L.​ (2010) 
    Journal of the Royal Statistical Society. Series C, Applied statistics, 60(1) pp. 51​-70​.​ DOI: https://doi.org/10.1111/j.1467-9876.2010.00723.x 
    ​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
    ​ ​Validation of the German Revised Addenbrooke’s Cognitive Examination for Detecting Mild Cognitive Impairment, Mild Dementia in Alzheimer’s Disease and Frontotemporal Lobar Degeneration​
    Alexopoulos, P.; Ebert, A. ; Richter-Schmidinger, T.; Schöll, E.; Natale, B.; Aguilar, C. A. & Gourzis, P. et al.​ (2010) 
    Dementia and Geriatric Cognitive Disorders, 29(5) pp. 448​-456​.​ DOI: https://doi.org/10.1159/000312685 
    ​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
    ​ ​Semiparametric Regression​
    Kneib, T. ​ (2010)
    In:​Leidl, R.; Hartmann, Alexander K.​ (Eds.), Modern Comutational Science 2010. ​Oldenburg University Press.
    ​Details 
  • 2010 | Journal Article
    ​ ​Decomposing environmental, spatial, and spatiotemporal components of species distributions​
    Hothorn, T.; Müller, J.; Schröder, B.; Kneib, T.   & Brandl, R.​ (2010) 
    Ecological Monographs, 81(2) pp. 329​-347​.​ DOI: https://doi.org/10.1890/10-0602.1 
    ​Details  DOI 
  • 2010 | Journal Article
    ​ ​Bayesian geoadditive sample selection models​
    Wiesenfarth, M.   & Kneib, T. ​ (2010) 
    Journal of the Royal Statistical Society. Series C, Applied statistics, 59(3) pp. 381​-404​.​ DOI: https://doi.org/10.1111/j.1467-9876.2009.00698.x 
    ​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 | Book Chapter
    ​ ​Exploratory Data Analysis​
    Kneib, T. ​ (2010)
    In:​Leidl, R.; Hartmann, Alexander K.​ (Eds.), Modern Comutational Science 2010. ​Oldenburg University Press.
    ​Details 
  • 2010 | Journal Article
    ​ ​Influence of brain-derived neurotrophic-factor and apolipoprotein E genetic variants on hippocampal volume and memory performance in healthy young adults​
    Richter-Schmidinger, T.; Alexopoulos, P.; Horn, M.; Maus, S.; Reichel, M.; Rhein, C. & Lewczuk, P.  et al.​ (2010) 
    Journal of Neural Transmission, 118(2) pp. 249​-257​.​ DOI: https://doi.org/10.1007/s00702-010-0539-8 
    ​Details  DOI 
  • 2010 | Journal Article
    ​ ​On the behaviour of marginal and conditional AIC in linear mixed models​
    Greven, S. & Kneib, T. ​ (2010) 
    Biometrika, 97(4) pp. 773​-789​.​ DOI: https://doi.org/10.1093/biomet/asq042 
    ​Details  DOI 
  • 2010 | Book Chapter
    ​ ​Generalized Semiparametric Regression with Covariates Measured with Error​
    Kneib, T. ; Brezger, A.& Crainiceanu, C. M.​ (2010)
    In: Statistical modelling and regression structures pp. 133​-154.  DOI: https://doi.org/10.1007/978-3-7908-2413-1_8 
    ​Details  DOI 
  • 2010 | Journal Article
    ​ ​Simultaneous Confidence Bands for Penalized Spline Estimators​
    Krivobokova, T. ; Kneib, T.   & Claeskens, G.​ (2010) 
    Journal of the American Statistical Association, 105(490) pp. 852​-863​.​ DOI: https://doi.org/10.1198/jasa.2010.tm09165 
    ​Details  DOI 
  • 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 | Preprint
    ​ ​On the Behavior of Marginal and Conditional Akaike Information Criteria in Linear Mixed Models​
    Kneib, T.  & Greven, S.​ (2009)
    ​Details 
  • 2009 | Preprint
    ​ ​Mathematische Grundlagen der Angewandten Statistik​
    Kneib, T. ​ (2009)
    ​Details 
  • 2009 | Journal Article
    ​ ​A general approach to the analysis of habitat selection​
    Kneib, T. ; Knauer, F. & Küchenhoff, H.​ (2009) 
    Environmental and Ecological Statistics, 18(1) pp. 1​-25​.​ DOI: https://doi.org/10.1007/s10651-009-0115-2 
    ​Details  DOI 
  • 2009 | Book Chapter
    ​ ​Nichtparametrische Regression​
    Fahrmeir, L.; Kneib, T.  & Lang, S.​ (2009)
    In: Regression: Modelle, Methoden und Anwendungen pp. 291​-398. (2. ed.). ​Berlin: ​Springer. DOI: https://doi.org/10.1007/978-3-642-01837-4_7 
    ​Details  DOI 
  • 2009 | Journal Article
    ​ ​Locally adaptive Bayesian P-splines with a Normal-Exponential-Gamma prior​
    Scheipl, F. & Kneib, T. ​ (2009) 
    Computational Statistics & Data Analysis, 53(10) pp. 3533​-3552​.​ DOI: https://doi.org/10.1016/j.csda.2009.03.009 
    ​Details  DOI 
  • 2009 | Book Chapter
    ​ ​Regressionsmodelle​
    Fahrmeir, L.; Kneib, T.  & Lang, S.​ (2009)
    In: Regression: Modelle, Methoden und Anwendungen pp. 19​-58. (2. ed.). ​Berlin: ​Springer. DOI: https://doi.org/10.1007/978-3-642-01837-4_2 
    ​Details  DOI 
  • 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 | Book Chapter
    ​ ​Strukturiert-additive Regression​
    Fahrmeir, L.; Kneib, T.  & Lang, S.​ (2009)
    In: Regression: Modelle, Methoden und Anwendungen pp. 399​-443. (2. ed.). ​Berlin: ​Springer. DOI: https://doi.org/10.1007/978-3-642-01837-4_8 
    ​Details  DOI 
  • 2009 | Book Chapter
    ​ ​Kategoriale Regressionsmodelle​
    Fahrmeir, L.; Kneib, T.  & Lang, S.​ (2009)
    In: Regression: Modelle, Methoden und Anwendungen pp. 235​-252. (2. ed.). ​Berlin: ​Springer. DOI: https://doi.org/10.1007/978-3-642-01837-4_5 
    ​Details  DOI 
  • 2009 | Monograph
    ​ ​Regression: ​Modelle, Methoden und Anwendungen​ ​(2. ed.) 
    Fahrmeir, L.; Kneib, T.  & Lang, S.​ (2009)
    Berlin​: Springer. DOI: https://doi.org/10.1007/978-3-642-01837-4 
    ​Details  DOI 
  • 2009 | Book Chapter
    ​ ​Generalisierte lineare Modelle​
    Fahrmeir, L.; Kneib, T.  & Lang, S.​ (2009)
    In: Regression: Modelle, Methoden und Anwendungen pp. 189​-234. (2. ed.). ​Berlin: ​Springer. DOI: https://doi.org/10.1007/978-3-642-01837-4_4 
    ​Details  DOI 
  • 2009 | Book Chapter
    ​ ​Lineare Regressionsmodelle​
    Fahrmeir, L.; Kneib, T.  & Lang, S.​ (2009)
    In: Regression: Modelle, Methoden und Anwendungen pp. 59​-188. (2. ed.). ​Berlin: ​Springer. DOI: https://doi.org/10.1007/978-3-642-01837-4_3 
    ​Details  DOI 
  • 2009 | Journal Article
    ​ ​PLASMA HOMOCYSTEINE AND CEREBROSPINAL FLUID NEURODEGENERATION BIOMARKERS IN MILD COGNITIVE IMPAIRMENT AND DEMENTIA​
    Alexopoulos, P.; Günther, F.; Popp, J.; Jessen, F.; Peters, O.; Wolf, S. & Kneib, T.  et al.​ (2009) 
    Journal of the American Geriatrics Society, 57(4) pp. 737​-739​.​ DOI: https://doi.org/10.1111/j.1532-5415.2009.02212.x 
    ​Details  DOI 
  • 2009 | Preprint
    ​ ​Additive Quantile Regression for the Analysis of Childhood Malnutrition​
    Kneib, T. ​ (2009)
    ​Details 
  • 2009 | Lecture
    ​ ​Statistical Modelling Based on Structured Additive Regression​
    Kneib, T. ​ (2009) 2009-01-16​
    ​Details 
  • 2009 | Journal Article
    ​ ​Estimation of the extinction risk for high-montane species as a consequence of global warming and assessment of their suitability as cross-taxon indicators​
    Bässler, C.; Müller, J.; Hothorn, T.; Kneib, T. ; Badeck, F. & Dziock, F.​ (2009) 
    Ecological Indicators, 10(2) pp. 341​-352​.​ DOI: https://doi.org/10.1016/j.ecolind.2009.06.014 
    ​Details  DOI 
  • 2009 | Preprint
    ​ ​BayesX and INLA - Opponents or Partners?​
    Kneib, T. ​ (2009)
    ​Details 
  • 2009 | Journal Article
    ​ ​Bayesian regularisation in structured additive regression: a unifying perspective on shrinkage, smoothing and predictor selection​
    Fahrmeir, L.; Kneib, T.   & Konrath, S.​ (2009) 
    Statistics and Computing, 20(2) pp. 203​-219​.​ DOI: https://doi.org/10.1007/s11222-009-9158-3 
    ​Details  DOI 
  • 2009 | Journal Article
    ​ ​Discussion on "Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations" by Rue, H., Martino, S. and Chopin, N​
    Fahrmeir, L. & Kneib, T. ​ (2009) 
    Journal of the Royal Statistical Society B: STATISTICAL METHODOLOGY, 71(2).​
    ​Details 
  • 2009 | Book Chapter
    ​ ​Gemischte Modelle​
    Fahrmeir, L.; Kneib, T.  & Lang, S.​ (2009)
    In:​Fahrmeir, Ludwig; Kneib, Thomas; Lang, Stefan​ (Eds.), Regression: Modelle, Methoden und Anwendungen pp. 253​-290. ​Berlin, Heidelberg: ​Springer. DOI: https://doi.org/10.1007/978-3-642-01837-4_6 
    ​Details  DOI 
  • 2009 | Book Chapter
    ​ ​Exploratory Data Analysis​
    Kneib, T. ​ (2009)
    In:​Leidl, R.; Hartmann, Alexander K.​ (Eds.), Modern Comutational Science 2009. ​Oldenburg University Press.
    ​Details 
  • 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 | Report
    ​ ​Bayesian Regularisation in Structured Additive Regression Models for Survival Data​
    Konrath, S.; Kneib, T.  & Fahrmeir, L.​ (2008). DOI: https://doi.org/10.5282/ubm/epub.5732 
    ​Details  DOI 
  • 2008 | Preprint
    ​ ​CoxFlexBoost: Fitting Structured Survival Models​
    Hofner, B.; Hothorn, T.& Kneib, T. ​ (2008)
    ​Details 
  • 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 | Book Chapter
    ​ ​On the Identification of Trend and Correlation in Temporal and Spatial Regression​
    Fahrmeir, L.& Kneib, T. ​ (2008)
    In:​Heumann, Christian​ (Ed.), Recent Advances in Linear Models and Related Areas: Essays in Honour of Helge Toutenburg pp. 1​-27. ​Heidelberg: ​Physica-Verlag HD. DOI: https://doi.org/10.1007/978-3-7908-2064-5_1 
    ​Details  DOI 
  • 2008 | Preprint
    ​ ​Semiparametrische Multinomiale Logit-Modelle zur Markenwahl-Analyse​
    Kneib, T. ​ (2008)
    ​Details 
  • 2008 | Preprint
    ​ ​Suchergebnisse Webergebnisse Zeitvariierende Effekte in Markenwahl-Modellen​
    Kneib, T. ​ (2008)
    ​Details 
  • 2008 | Preprint
    ​ ​Time-varying Coe-cients in Brand Choice Modelling​
    Kneib, T. ​ (2008)
    ​Details 
  • 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 | Preprint
    ​ ​Bayesianische Regularisierung in semiparametrischen Regressionsmodellen​
    Kneib, T. ​ (2008)
    ​Details 
  • 2008 | Journal Article
    ​ ​ACTIVITY-GUIDED ANTITHROMBIN III THERAPY IN SEVERE SURGICAL SEPSIS​
    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 
  • 2008 | Preprint
    ​ ​Boosting Geoadditive Regression Models​
    Kneib, T. ; Hothorn, T.& Trutz, G.​ (2008)
    ​Details 
  • 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 | Preprint
    ​ ​mboost - Componentwise Boosting for Generalised Regression Models​
    Kneib, T.  & Hothorn, T.​ (2008)
    ​Details 
  • 2008 | Journal Article
    ​ ​Variable Selection and Model Choice in Geoadditive Regression Models​
    Kneib, T. ; Hothorn, T. & Tutz, G.​ (2008) 
    Biometrics, 65(2) pp. 626​-634​.​ DOI: https://doi.org/10.1111/j.1541-0420.2008.01112.x 
    ​Details  DOI 
  • 2008 | Journal Article
    ​ ​Spatial smoothing techniques for the assessment of habitat suitability​
    Kneib, T. ; Müller, J. & Hothorn, T.​ (2008) 
    Environmental and Ecological Statistics, 15(3) pp. 343​-364​.​ DOI: https://doi.org/10.1007/s10651-008-0092-x 
    ​Details  DOI 
  • 2008 | Journal Article
    ​ ​Use of Penalized Splines in Extended Cox-Type Additive Hazard Regression to Flexibly Estimate the Effect of Time-varying Serum Uric Acid on Risk of Cancer Incidence: A Prospective, Population-Based Study in 78,850 Men​
    Strasak, A. M.; Lang, S.; Kneib, T. ; Brant, L. J.; Klenk, J.; Hilbe, W. & Oberaigner, W. et al.​ (2008) 
    Annals of Epidemiology, 19(1) pp. 15​-24​.​ DOI: https://doi.org/10.1016/j.annepidem.2008.08.009 
    ​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 | Report
    ​ ​BayesX - Bayesian inference in structured additive regression​
    Kneib, T. ; Belitz, C.; Brezger, A.& Lang, S.​ (2007)
    ​Details 
  • 2007 | Monograph
    ​ ​Regression: ​Modelle, Methoden und Anwendungen ; mit 51 Tabellen​ ​
    Fahrmeir, L.; Kneib, T.  & Lang, S.​ (2007)
    Berlin​: Springer. DOI: https://doi.org/10.1007/978-3-540-33933-5 
    ​Details  DOI 
  • 2007 | Book Chapter
    ​ ​Kategoriale Regressionsmodelle​
    Fahrmeir, L.; Kneib, T.  & Lang, S.​ (2007)
    In: Regression: Modelle, Methoden und Anwendungen pp. 235​-252. ​Berlin: ​Springer. DOI: https://doi.org/10.1007/978-3-540-33933-5_5 
    ​Details  DOI 
  • 2007 | Book Chapter
    ​ ​Strukturiert-additive Regression​
    Fahrmeir, L.; Kneib, T.  & Lang, S.​ (2007)
    In: Regression: Modelle, Methoden und Anwendungen pp. 399​-443. ​Berlin: ​Springer. DOI: https://doi.org/10.1007/978-3-540-33933-5_8 
    ​Details  DOI 
  • 2007 | Book Chapter
    ​ ​Regressionsmodelle​
    Fahrmeir, L.; Kneib, T.  & Lang, S.​ (2007)
    In: Regression: Modelle, Methoden und Anwendungen pp. 19​-58. ​Berlin: ​Springer. DOI: https://doi.org/10.1007/978-3-540-33933-5_2 
    ​Details  DOI 
  • 2007 | Book Chapter
    ​ ​Generalisierte lineare Modelle​
    Fahrmeir, L.; Kneib, T.  & Lang, S.​ (2007)
    In: Regression: Modelle, Methoden und Anwendungen pp. 189​-234. ​Berlin: ​Springer. DOI: https://doi.org/10.1007/978-3-540-33933-5_4 
    ​Details  DOI 
  • 2007 | Book Chapter
    ​ ​Nichtparametrische Regression​
    Fahrmeir, L.; Kneib, T.  & Lang, S.​ (2007)
    In: Regression: Modelle, Methoden und Anwendungen pp. 291​-398. ​Berlin: ​Springer. DOI: https://doi.org/10.1007/978-3-540-33933-5_7 
    ​Details  DOI 
  • 2007 | Book Chapter
    ​ ​Lineare Regressionsmodelle​
    Fahrmeir, L.; Kneib, T.  & Lang, S.​ (2007)
    In: Regression: Modelle, Methoden und Anwendungen pp. 59​-188. ​Berlin: ​Springer. DOI: https://doi.org/10.1007/978-3-540-33933-5_3 
    ​Details  DOI 
  • 2007 | Preprint
    ​ ​Semiparametric Event History Models for Analyzing Human Sleep Data​
    Kneib, T.  & Hennerfeind, A.​ (2007)
    ​Details 
  • 2007 | Journal Article
    ​ ​Semiparametric multinomial logit models for analysing consumer choice behaviour​
    Kneib, T. ; Baumgartner, B. & Steiner, W. J.​ (2007) 
    AStA Advances in Statistical Analysis, 91(3) pp. 225​-244​.​ DOI: https://doi.org/10.1007/s10182-007-0033-2 
    ​Details  DOI 
  • 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 
  • 2006 | Journal Article
    ​ ​Mixed model-based inference in geoadditive hazard regression for interval-censored survival times​
    Kneib, T. ​ (2006) 
    Computational Statistics & Data Analysis, 51(2) pp. 777​-792​.​ DOI: https://doi.org/10.1016/j.csda.2006.06.019 
    ​Details  DOI 
  • 2006 | Preprint
    ​ ​Empirical Bayes Inference in Structured Hazard Regression​
    Kneib, T. ​ (2006)
    ​Details 
  • 2006 | Preprint
    ​ ​Bayesian Structured Hazard Regression​
    Hennerfeind, A.; Fahrmeir, L.& Kneib, T. ​ (2006)
    ​Details 
  • 2006 | Preprint
    ​ ​BayesX: Bayesianische Inferenz in strukturiert additiven Regressionsmodellen​
    Kneib, T. ​ (2006)
    ​Details 
  • 2006 | Journal Article
    ​ ​Structured Additive Regression for Categorical Space-Time Data: A Mixed Model Approach​
    Kneib, T.   & Fahrmeir, L.​ (2006) 
    Biometrics, 62(1) pp. 109​-118​.​ DOI: https://doi.org/10.1111/j.1541-0420.2005.00392.x 
    ​Details  DOI 
  • 2006 | Journal Article
    ​ ​A Mixed Model Approach for Geoadditive Hazard Regression​
    Kneib, T.   & Fahrmeir, L.​ (2006) 
    Scandinavian Journal of Statistics, 34(1) pp. 207​-228​.​ DOI: https://doi.org/10.1111/j.1467-9469.2006.00524.x 
    ​Details  DOI 
  • 2005 | Preprint
    ​ ​Supplement to "Structured additive regression for categorical space-time data: A mixed model approach"​
    Kneib, T.  & Fahrmeir, L.​ (2005)
    ​Details 
  • 2005 | Preprint
    ​ ​Analysing geoadditive regression data: a mixed model approach​
    Kneib, T. ​ (2005)
    ​Details 
  • 2005 | Preprint
    ​ ​Gemischte Modelle zur Schatzung geoadditiver Regressionsmodelle​
    Kneib, T.  & Fahrmeir, L.​ (2005)
    ​Details 
  • 2004 | Preprint
    ​ ​A mixed model approach for structured hazard regression​
    Kneib, T.  & Fahrmeir, L.​ (2004). DOI: https://doi.org/10.5282/ubm/epub.1770 
    ​Details  DOI 
  • 2004 | Preprint
    ​ ​Structured additive regression for multicategorical space-time data: A mixed model approach​
    Kneib, T.  & Fahrmeir, L.​ (2004). DOI: https://doi.org/10.5282/ubm/epub.1748 
    ​Details  DOI 
  • 2004 | Report
    ​ ​Bayesian semiparametric regression based on mixed model methodology: A tutorial​
    Kneib, T. ; Lang, S.& Brezger, A.​ (2004)
    ​Details 
  • 2004 | Journal Article
    ​ ​Penalized structured additive regression for space-time data: a Bayesian perspective​
    Fahrmeir, L.; Kneib, T.   & Lang, S.​ (2004) 
    Statistica Sinica, 14 pp. 731​-761​.​
    ​Details 
  • 2004 | Journal Article
    ​ ​Penalized structured additive regression for space-time data​
    Kneib, T. ​ (2004) 
    Biometrical Journal, 46(S1) pp. 1​-1​.​ DOI: https://doi.org/10.1002/bimj.200490310 
    ​Details  DOI 
  • 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 
  • 2004 | Preprint
    ​ ​Modelling geoadditive survival data​
    Kneib, T. ​ (2004)
    ​Details 
  • 2004 | Preprint
    ​ ​A general mixed model approach for spatio-temporal regression data​
    Kneib, T. ; Fahrmeir, L.& Lang, S.​ (2004)
    ​Details 
  • 2004 | Preprint
    ​ ​Penalized structured additive regression for multicategorical space-time data​
    Kneib, T. ​ (2004)
    ​Details 
  • 2003 | Report
    ​ ​BayesX Manuals: ​Reference Manual, Methodology Manual, Tutorials Manual​
    Kneib, T. ; Bretzger, A.& Lang, S.​ (2003)
    ​Details 
  • 2003 | Thesis | Diploma Thesis
    ​ ​Bayes-Inferenz in generalisierten geoadditiven gemischten Modellen​
    Kneib, T. ​ (2003)
    Ludwig-Maximilians-Universität München, Institut für Statistik. 
    München​
    ​Details 
  • 2003 | Report
    ​ ​Restricted maximum likelihood estimation of variance parameters in generalized linear mixed models​
    Kneib, T. ​ (2003)
    ​Details 

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