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
    ​ ​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
    ​ ​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
    ​ ​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 
  • 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 
  • 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 | 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
    ​ ​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
    ​ ​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
    ​ ​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
    ​ ​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 
  • 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
    ​ ​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
    ​ ​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 | 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
    ​ ​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 
  • 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 | 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
    ​ ​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
    ​ ​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 | 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
    ​ ​Rejoinder​
    Kneib, T. ​ (2013) 
    Statistical Modelling, 13(4) pp. 373​-385​.​ DOI: https://doi.org/10.1177/1471082X13494531 
    ​Details  DOI  WoS 
  • 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 | 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 

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