Publikationsliste

Numerical solution of a parameter estimation problem arising in Prompt-Gamma Neutron Activation Analysis
Jesser, A.; Krycki, K.; Frank, M.
2024. Applied Mathematics in Science and Engineering, 32 (1), Article no: 2336170. doi:10.1080/27690911.2024.2336170

A stochastic Galerkin lattice Boltzmann method for incompressible fluid flows with uncertainties
Zhong, M.; Xiao, T.; Krause, M. J.; Frank, M.; Simonis, S.
2024. Journal of Computational Physics, 517, 113344. doi:10.1016/j.jcp.2024.113344

A Fully Parallelized and Budgeted Multilevel Monte Carlo Method and the Application to Acoustic Waves
Baumgarten, N.; Krumscheid, S.; Wieners, C.
2024. SIAM/ASA Journal on Uncertainty Quantification, 12 (3), 901–931. doi:10.1137/23M1588354

Emotion Regulation in Obsessive-Compulsive Disorder: An Ecological Momentary Assessment Study
Bischof, C.; Hohensee, N.; Dietel, F. A.; Doebler, P.; Klein, N.; Buhlmann, U.
2024. Behavior Therapy, 55 (5), 935–949. doi:10.1016/j.beth.2024.01.011

Divergent Associations of Slow‐Wave Sleep versus Rapid Eye Movement Sleep with Plasma Amyloid‐Beta
Rosenblum, Y.; Pereira, M.; Stange, O.; Weber, F. D.; Bovy, L.; Tzioridou, S.; Lancini, E.; Neville, D. A.; Klein, N.; de Wolff, T.; Stritzke, M.; Kersten, I.; Uhr, M.; Claassen, J. A. H. R.; Steiger, A.; Verbeek, M. M.; Dresler, M.
2024. Annals of Neurology, 96 (1), 46–60. doi:10.1002/ana.26935

Scalable multiscale-spectral GFEM with an application to composite aero-structures
Bénézech, J.; Seelinger, L.; Bastian, P.; Butler, R.; Dodwell, T.; Ma, C.; Scheichl, R.
2024. Journal of Computational Physics, 508, Art.-Nr.: 113013. doi:10.1016/j.jcp.2024.113013

Distributional Regression for Data Analysis
Klein, N.
2024. Annual Review of Statistics and Its Application, 11, 321–346. doi:10.1146/annurev-statistics-040722-053607

Physics Informed Neural Networks for Neutron Transport in Large Sample Prompt Gamma Activation Analysis
Jesser, A.; Krycki, K.
2024, April 10. Mashine Learning Conference for X-Ray and Neutron-Based Experiments (2024), Garching bei München, Deutschland, 8.–10. April 2024

Bayesian Conditional Transformation Models
Carlan, M.; Kneib, T.; Klein, N.
2024. Journal of the American Statistical Association, 119 (546), 1360–1373. doi:10.1080/01621459.2023.2191820

Bivariate Analysis of Birth Weight and Gestational Age by Bayesian Distributional Regression with Copulas
Rathjens, J.; Kolbe, A.; Hölzer, J.; Ickstadt, K.; Klein, N.
2024. Statistics in Biosciences, 16 (1), 290–317. doi:10.1007/s12561-023-09396-4

Flexible specification testing in quantile regression models
Kutzker, T.; Klein, N.; Wied, D.
2024. Scandinavian Journal of Statistics, 51 (1), 355–383. doi:10.1111/sjos.12671

Projektkurs für Mädels: Mit Mathe und KI reale Probleme lösen
Hofmann, S.; Schönbrodt, S.
2024. Mitteilungen der Deutschen Mathematiker-Vereinigung, 32 (2), 124–126. doi:10.1515/dmvm-2024-0037

BannMI deciphers potential n -to-1 information transduction in signaling pathways to unravel message of intrinsic apoptosis
Schmidt, B.; Sers, C.; Klein, N.
2024. (T. Lengauer, Hrsg.) Bioinformatics Advances, 4 (1), Article no: vbad175. doi:10.1093/bioadv/vbad175

Semi-Structured Distributional Regression
Rügamer, D.; Kolb, C.; Klein, N.
2024. The American Statistician, 78 (1), 88–99. doi:10.1080/00031305.2022.2164054

Informed Spectral Normalized Gaussian Processes for Trajectory Prediction
Schlauch, C.; Wirth, C.; Klein, N.
2024. doi:10.5445/IR/1000175967

Sparse Explanations of Neural Networks Using Pruned Layer-Wise Relevance Propagation
Yanez Sarmiento, P.; Witzke, S.; Klein, N.; Renard, B. Y.
2024. Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Research Track: European Conference, ECML PKDD 2024, Vilnius, Lithuania, September 9–13, 2024, Proceedings, Part IV, 336–351, Springer. doi:10.1007/978-3-031-70359-1_20

Investigating Calibration and Corruption Robustness of Post-hoc Pruned Perception CNNs: An Image Classification Benchmark Study
Mitra, P.; Schwalbe, G.; Klein, N.
2024. Proceedings of the IEEE/CVF Computer Vision and Pattern Recognition Conference (CVPR), 17th - 21st June 2024, Seattle, 3542–3552

Boosting distributional copula regression for bivariate time-to-event data
Sanchez, G. B.; Klein, N.; Groll, A.; Mayr, A.
2024. Proceedings of the 38th International Workshop on Statistical Modelling, 14th - 19th July, 2024, Durham, UK. Eds.: Jochen Einbeck, Reza Drikvandi, Georgios Karagiannis, Konstantinos Perrakis, Qing Zhang, 75–80

Cost-Sensitive Uncertainty-Based Failure Recognition for Object Detection
Kassem-Sbeyti, M.; Karg, M.; Wirth, C.; Klein, N.; Albayrak, S.
2024. Uncertainty in Artificial Intelligence, 1890–1900

Intergenerational Social Mobility in the United States: A Multivariate Analysis Using Distributional Regression
März, A.; Klein, N.; Kneib, T.; Mußhoff, O.
2024. Advanced Statistical Methods in Process Monitoring, Finance, and Environmental Science : Essays in Honour of Wolfgang Schmid. Ed.: S. Knoth, 295–335, Springer Nature Switzerland. doi:10.1007/978-3-031-69111-9_15

Generalized Additive Models for Location, Scale and Shape: A Distributional Regression Approach, with Applications
Stasinopoulos, M. D.; Kneib, T.; Klein, N.; Mayr, A.; Heller, G. Z.
2024. Cambridge University Press (CUP). doi:10.1017/9781009410076

Solving stationary nonlinear Fokker-Planck equations via sampling
Li, L.; Tang, Y.; Zhang, J.
2024. SIAM journal on applied mathematics

bamlss - Bayesian Additive Models for Location, Scale, and Shape (and Beyond)
Umlauf, N.; Klein, N.; Zeileis, A.
2024

Ginkgo - A math library designed to accelerate Exascale Computing Project science applications
Cojean, T.; Nayak, P.; Ribizel, T.; Beams, N.; Mike Tsai, Y.-H.; Koch, M.; Göbel, F.; Grützmacher, T.; Anzt, H.
2024. The International Journal of High Performance Computing Applications. doi:10.1177/10943420241268323

From Counting Stations to City-Wide Estimates: Data-Driven Bicycle Volume Extrapolation
Kaiser, S. K.; Klein, N.; Kaack, L. H.
2024. arxiv

Scalable Estimation for Structured Additive Distributional Regression
Umlauf, N.; Seiler, J.; Wetscher, M.; Simon, T.; Lang, S.; Klein, N.
2024. Journal of Computational and Graphical Statistics, 1–17. doi:10.1080/10618600.2024.2388604

Wortvorschläge beim Chatten : KI und natürliche Sprachverarbeitung im Stochastikunterricht
Hofmann, S.; Frank, M.
2024. Mathematik lehren, (244), 24–29

Towards a platform-portable linear algebra backend for OpenFOAM
Olenik, G.; Koch, M.; Boutanios, Z.; Anzt, H.
2024. Meccanica. doi:10.1007/s11012-024-01806-1

Semantic Segmentation of Crops and Weeds with Probabilistic Modeling and Uncertainty Quantification
Celikkan, E.; Saberioon, M.; Herold, M.; Klein, N.
2023. Proceedings of the IEEE/CVF International Conference on Computer Vision, Paris, 2nd - 6th October 2023, 582–592, IEEEXplore. doi:10.1109/ICCVW60793.2023.00065

Accounting for time dependency in meta‐analyses of concordance probability estimates
Schmid, M.; Friede, T.; Klein, N.; Weinhold, L.
2023. Research Synthesis Methods, 14 (6), 807–823. doi:10.1002/jrsm.1655

Boosting Distributional Copula Regression
Hans, N.; Klein, N.; Faschingbauer, F.; Schneider, M.; Mayr, A.
2023. Biometrics, 79 (3), 2298–2310. doi:10.1111/biom.13765

Approximate Bayesian Computation for Parameter Identification in Computational Mechanics
Faes, M. G. R.; Klein, N.; Pauly, M.; Valdebenito, M. A.; Misraji, M. A.
2023. 14th International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP14, Dublin, Ireland, July 9-13, 2023, 1–7

Marginally calibrated response distributions for end-to-end learning in autonomous driving
Hoffmann, C.; Klein, N.
2023. The Annals of Applied Statistics, 17 (2), 1740–1763. doi:10.1214/22-AOAS1693

Deep distributional time series models and the probabilistic forecasting of intraday electricity prices
Klein, N.; Smith, M. S.; Nott, D. J.
2023. Journal of Applied Econometrics, 38 (4), 493–511. doi:10.1002/jae.2959

Maschinelles Lernen im Schulunterricht am Beispiel einer problemorientierten Lerneinheit zur Wortvorhersage
Hofmann, S.; Frank, M.
2023. 56. Jahrestagung der Gesellschaft für Didaktik der Mathematik, 157–160, WTM Verlag für wissenschaftliche Texte und Medien. doi:10.17877/DE290R-23317

Boosting multivariate structured additive distributional regression models
Strömer, A.; Klein, N.; Staerk, C.; Klinkhammer, H.; Mayr, A.
2023. Statistics in Medicine, 42 (11), 1779–1801. doi:10.1002/sim.9699

Modelling intra-annual tree stem growth with a distributional regression approach for Gaussian process responses
Riebl, H.; Klein, N.; Kneib, T.
2023. Journal of the Royal Statistical Society Series C: Applied Statistics, 72 (2), 414–433. doi:10.1093/jrsssc/qlad015

Dropout Regularization in Extended Generalized Linear Models based on Double Exponential Families
Schwienhorst, B. L.; Kock, L.; Klein, N.; Nott, D. J.
2023. arxiv

Overcoming the Limitations of Localization Uncertainty: Efficient and Exact Non-linear Post-processing and Calibration
Kassem Sbeyti, M.; Karg, M.; Wirth, C.; Nowzad, A.; Albayrak, S.
2023. Machine Learning and Knowledge Discovery in Databases: Research Track – European Conference, ECML PKDD 2023, Turin, Italy, September 18–22, 2023, Proceedings, Part V. Ed.: D. Koutra, 52–68, Springer Nature Switzerland. doi:10.1007/978-3-031-43424-2_4

Boosting Distributional Soft Regression Trees
Umlauf, N.; Seiler, J.; Wetscher, M.; Klein, N.
2023. Proceedings of the 37th International Workshop on Statistical Modelling. Ed.: E. Bergherr, 311–316, Technische Universität Dortmund (TU Dortmund)

Complexity Reduction via Deselection for Boosting Distributional Copula Regression
Strömer, A.; Klein, N.; Staerk, C.; Klinkhammer, H.; Mayr, A.
2023. Proceedings of the 37th International Workshop on Statistical Modelling. Ed.: E. Bergherr, 300–304, Technische Universität Dortmund (TU Dortmund)

Informed Priors for Knowledge Integration in Trajectory Prediction
Schlauch, C.; Klein, N.; Wirth, C.; Klein, N.
2023. D. Koutra (Hrsg.), Machine Learning and Knowledge Discovery in Databases: Research Track : European Conference, ECML PKDD 2023, Turin, Italy, September 18–22, 2023, Proceedings, Part V, Hrsg.: D. Koutra, C. Plant, R. M. Gomez, E. Baralis, F. Bonchi, 392–407, Springer Nature Switzerland. doi:10.1007/978-3-031-43424-2_24

The Consequences of not Completing the Generational Cohort in Estimating Age-at-Menopause
Martins, R.; Sousa, B. de; Kneib, T.; Hohberg, M.; Klein, N.; Duarte, E.; Rodrigues, V.
2023. Proceedings of the 37th International Workshop on Statistical Modelling : July 17-21, 2023, Dortmund, Germany, Ed.: E. Bergherr, A. Groll, A. Mayr, 502–506

A Distributional Regression Approach for Gaussian Process Responses
Kneib, T.; Riebl, H.; Klein, N.
2023. Proceedings of the 37th International Workshop on Statistical Modelling July 17-21, 2023 - Dortmund, Germany. Ed.: E. Bergherr, 279–284, Technische Universität Dortmund (TU Dortmund)

Predicting cycling traffic in cities: Is bikesharing data representative of the cycling volume
Kaiser, S. K.; Klein, N.; Kaack, L. H.
2023. 11th International Conference on Learning Representations (ICLR 2023): Tackling Climate Change with Machine Learning Workshop

deepregression : A Flexible Neural Network Framework for Semi-Structured Deep Distributional Regression
Rügamer, D.; Kolb, C.; Fritz, C.; Pfisterer, F.; Kopper, P.; Bischl, B.; Shen, R.; Bukas, C.; Barros de Andrade e Sousa, L.; Thalmeier, D.; Baumann, P. F. M.; Kook, L.; Klein, N.; Müller, C. L.
2023. Journal of Statistical Software, 105 (2). doi:10.18637/jss.v105.i02

Is age at menopause decreasing? – The consequences of not completing the generational cohort
Martins, R.; Sousa, B. de; Kneib, T.; Hohberg, M.; Klein, N.; Duarte, E.; Rodrigues, V.
2022. BMC Medical Research Methodology, 22 (1), Art.-Nr. 187. doi:10.1186/s12874-022-01658-x

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), Art.-Nr. 73. doi:10.1007/s11222-022-10136-9

Variational inference and sparsity in high-dimensional deep Gaussian mixture models
Kock, L.; Klein, N.; Nott, D. J.
2022. Statistics and Computing, 32 (5), Art.-Nr. 70. doi:10.1007/s11222-022-10132-z

Mitigating spatial confounding by explicitly correlating Gaussian random fields
Marques, I.; Kneib, T.; Klein, N.
2022. Environmetrics, 33 (5), Art.-Nr. e2727. doi:10.1002/env.2727

Mathematische Modellierungswochen – auch online
Schönbrodt, S.; Hofmann, S.
2022. Mitteilungen der Deutschen Mathematiker-Vereinigung, 30 (1), 46–50. doi:10.1515/dmvm-2022-0016

Correcting for sample selection bias in Bayesian distributional regression models
Wiemann, P. F. V.; Klein, N.; Kneib, T.
2022. Computational Statistics & Data Analysis, 168, 107382. doi:10.1016/j.csda.2021.107382

Multivariate conditional transformation models
Klein, N.; Hothorn, T.; Barbanti, L.; Kneib, T.
2022. Scandinavian Journal of Statistics, 49 (1), 116–142. doi:10.1111/sjos.12501

Review of guidance papers on regression modeling in statistical series of medical journals
Wallisch, C.; Bach, P.; Hafermann, L.; Klein, N.; Sauerbrei, W.; Steyerberg, E. W.; Heinze, G.; Rauch, G.
2022. (T. Mathes, Hrsg.) PLOS ONE, 17 (1), Art.-Nr.: e0262918. doi:10.1371/journal.pone.0262918

Using Background Knowledge from Preceding Studies for Building a Random Forest Prediction Model: A Plasmode Simulation Study
Hafermann, L.; Klein, N.; Rauch, G.; Kammer, M.; Heinze, G.
2022. Entropy, 24 (6), 847. doi:10.3390/e24060847

Neue Materialien für einen realitätsbezogenen Mathematikunterricht 9 – ISTRON-Schriftenreihe
Frank, M.; Roeckerath, C. (Hrsg.)
2022. Springer-Verlag. doi:10.1007/978-3-662-63647-3

Teaching data science in school: Digital learning material on predictive text systems
Hofmann, S.; Frank, M.
2022. Twelfth Congress of the European Society for Research in Mathematics Education (CERME12)

Non-destructive Material Characterization of Waste Packages with QUANTOM - 22208
Herbell, H.; Coquard, L.; Hummel, J.; Nordhardt, G.; Veltkamp, T.; Havenith, A.; Krycki, K.; Fu, B.; Helmes, C.; Doemeland, M.; Heidner, M.; Simons, F.; Köble, T.; Schumann, O.; Jesser, A.
2022

Partial Cross-Section Calculations for PGNAA Based on a Deterministic Neutron Transport Solver
Jesser, A.; Krycki, K.; Frank, M.
2022. Nuclear technology, 208 (7), 1114–1123. doi:10.1080/00295450.2021.2016018

bamlss : A Lego Toolbox for Flexible Bayesian Regression (and Beyond)
Umlauf, N.; Klein, N.; Simon, T.; Zeileis, A.
2021. Journal of Statistical Software, 100 (4). doi:10.18637/jss.v100.i04

Assessment and Adjustment of Approximate Inference Algorithms Using the Law of Total Variance
Yu, X.; Nott, D. J.; Tran, M.-N.; Klein, N.
2021. Journal of Computational and Graphical Statistics, 30 (4), 977–990. doi:10.1080/10618600.2021.1880921

Bayesian variable selection for non‐Gaussian responses: a marginally calibrated copula approach
Klein, N.; Smith, M. S.
2021. Biometrics, 77 (3), 809–823. doi:10.1111/biom.13355

Bayesian Inference for Regression Copulas
Smith, M. S.; Klein, N.
2021. Journal of Business & Economic Statistics, 39 (3), 712–728. doi:10.1080/07350015.2020.1721295

Bayesian Effect Selection in Structured Additive Distributional Regression Models
Klein, N.; Carlan, M.; Kneib, T.; Lang, S.; Wagner, H.
2021. Bayesian Analysis, 16 (2), 545–573. doi:10.1214/20-BA1214

Marginally Calibrated Deep Distributional Regression
Klein, N.; Nott, D. J.; Smith, M. S.
2021. Journal of Computational and Graphical Statistics, 30 (2), 467–483. doi:10.1080/10618600.2020.1807996

In search of lost edges: a case study on reconstructing financial networks
Lebacher, M.; Klein, N.; Kauermann, G.; Cook, S.
2021. The Journal of Network Theory in Finance, 29–61. doi:10.21314/JNTF.2019.058

Zerstörungsfreie stoffliche Beschreibung und Plausibilitätsprüfung radioaktiver Abfälle mittels QUANTOM
Coquard, L.; Nordhardt, G.; Hummel, J.; Havenith, A.; Krycki, K.; Hirsch, M.; Wangnick, M.; Fu, B.; Reisenhofer, F.; Hansmann, B.; Hansmann, T.; Helmes, C.; Dürr, M.; Rother, M.; Simons, F.; Köble, T.; Schumann, O.; Jesser, A.
2021. KONTEC GmbH

Non-stationary spatial regression for modelling monthly precipitation in Germany
Marques, I.; Klein, N.; Kneib, T.
2020. Spatial Statistics, 40, Art.-Nr.: 100386. doi:10.1016/j.spasta.2019.100386

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.
2020. Journal of Applied Statistics, 47 (11), 2066–2080. doi:10.1080/02664763.2019.1704226

Directional bivariate quantiles: a robust approach based on the cumulative distribution function
Klein, N.; Kneib, T.
2020. AStA Advances in Statistical Analysis, 104 (2), 225–260. doi:10.1007/s10182-019-00355-3

Cold War spy satellite images reveal long-term declines of a philopatric keystone species in response to cropland expansion
Munteanu, C.; Kamp, J.; Nita, M. D.; Klein, N.; Kraemer, B. M.; Müller, D.; Koshkina, A.; Prishchepov, A. V.; Kuemmerle, T.
2020. Proceedings of the Royal Society B: Biological Sciences, 287 (1927), 20192897. doi:10.1098/rspb.2019.2897

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), 513–539. doi:10.1016/j.jeconom.2019.07.003

Multivariate Conditional Transformation Models
Kneib, T.; Klein, N.; Hothorn, T.
2020. Proceedings of the 35th International Workshop on Statistical Modelling. Ed.: I. Irigoien, 131–136, Universidad del País Vasco

Enhanced variable selection for distributional regression
Strömer, A.; Weinhold, L.; Staerk, C.; Titze, S.; Klein, N.; Mayr, A.
2020. Proceedings of the 35th International Workshop on Statistical Modelling. Ed.: I. Irigoien, 233–237, Universidad del Pais Vasco

Introducing non-stationarity to wrapped Gaussian spatial responses with an application to wind direction
Marques, I.; Klein, N.; Kneib, T.
2020. Proceedings of the 35th International Workshop on Statistical Modelling. Ed.: I. Irigoien, 159–164, Universidad del País Vasco

Bayesian mixed binary-continuous copula regression with an application to childhood undernutrition
Klein, N.; Kneib, T.; Marra, G.; Radice, R.
2020. Flexible Bayesian Regression Modelling, 121–152, Elsevier. doi:10.1016/B978-0-12-815862-3.00011-1

Mixed discrete‐continuous regression—A novel approach based on weight functions
Michaelis, P.; Klein, N.; Kneib, T.
2020. Stat, 9 (1), Art.-Nr.: e277. doi:10.1002/sta4.277

Systematic review of education and practical guidance on regression modeling for medical researchers who lack a strong statistical background: Study protocol
Bach, P.; Wallisch, C.; Klein, N.; Hafermann, L.; Sauerbrei, W.; Steyerberg, E. W.; Heinze, G.; Rauch, G.
2020. (R. Bender, Hrsg.) PLOS ONE, 15 (12), e0241427. doi:10.1371/journal.pone.0241427

Implicit Copulas from Bayesian Regularized Regression Smoothers
Klein, N.; Smith, M. S.
2019. Bayesian Analysis, 14 (4), 1143–1171. doi:10.1214/18-BA1138

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.; González-Quintela, A.; Gude, F.
2019. Statistics in Medicine, 38 (27), 5161–5181. doi:10.1002/sim.8358

Multivariate effect priors in bivariate semiparametric recursive Gaussian models
Thaden, H.; Klein, N.; Kneib, T.
2019. Computational Statistics & Data Analysis, 137, 51–66. doi:10.1016/j.csda.2018.12.004

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, 28 (1), 55–59. doi:10.1007/s11749-019-00636-8

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, 28 (1), 1–39. doi:10.1007/s11749-019-00631-z

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), 413–436. doi:10.1002/sim.7985

Gaussian Process Responses in Distributional Regression
Riebl, H.; Klein, N.; Kneib, T.
2019. Proceedings of the 34th International Workshop on Statistical Modelling. Ed.: L.M. Machado. Vol. 2, 341–345, Statistical Modelling Society

Non-Stationary Spatial Regression for Modelling Monthly Precipitation in Germany
Marques, I.; Klein, N.; Kneib, T.
2019. Proceedings of the 34th International Workshop on Statistical Modelling. Vol 2. Ed.: L. Meira-Machado, 195–199

Modular Regression – A Lego System for Building Structured Additive Distributional Regression Models with Tensor Product Interactions
Kneib, T.; Klein, N.; Umlauf, N.; Lang, S.
2019. Proceedings of the 34th International Workshop on Statistical Modelling. Vol 1. Ed.: L. Meira-Machado, 214–219

Neural Network Regression with an Application to Leukaemia Survival Data – An Unstructured Distributional Approach
Klein, N.; Umlauf, N.; Simon, T.
2019. Proceedings of the 34th International Workshop on Statistical Modelling. Vol 1. Ed.: L. Meira-Machado, 157–160

Density regression via penalised splines dependent Dirichlet process mixture of normal models
Carvalho, V. de; Rodríguez-Álvarez, M.; Klein, N.
2019. Proceedings of the 34th International Workshop on Statistical Modelling. Vol 1. Ed.: L. Meira-Machado, 184–188

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, 107–118. doi:10.1016/j.envsoft.2018.03.008

More green space is related to less antidepressant prescription rates in the Netherlands: A Bayesian geoadditive quantile regression approach
Helbich, M.; Klein, N.; Roberts, H.; Hagedoorn, P.; Groenewegen, P. P.
2018. Environmental Research, 166, 290–297. doi:10.1016/j.envres.2018.06.010

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), 602–611. doi:10.1080/10618600.2017.1395343

Effect Selection in Distributional Regression
Klein, N.; Carlan, M.; Kneib, T.; Lang, S.; Wagner, H.
2018. Proceedings of the 33th International Workshop on Statistical Modelling. Vol. 1, 157–162, Statistical Modelling Society

Quality and resource efficiency in hospital service provision: A geoadditive stochastic frontier analysis of stroke quality of care in Germany
Pross, C.; Strumann, C.; Geissler, A.; Herwartz, H.; Klein, N.
2018. (A. Arrieta, Hrsg.) PLOS ONE, 13 (9), e0203017. doi:10.1371/journal.pone.0203017

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), 1101–1103. doi:10.1002/bimj.201700180

Studying the relationship between a woman’s reproductive lifespan and age at menarche using a Bayesian multivariate structured additive distributional regression model
Duarte, E.; Sousa, B. de; Cadarso-Suárez, C.; Klein, N.; Kneib, T.; Rodrigues, V.
2017. Biometrical Journal, 59 (6), 1232–1246. doi:10.1002/bimj.201600245

Boosting joint models for longitudinal and time‐to‐event data
Waldmann, E.; Taylor-Robinson, D.; Klein, N.; Kneib, T.; Pressler, T.; Schmid, M.; Mayr, A.
2017. Biometrical Journal, 59 (6), 1104–1121. doi:10.1002/bimj.201600158

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 (SI, 2), 419–433. doi:10.1016/j.spasta.2017.07.005

Bayesian Joint Modelling of Distributional Regression
Waldmann, E.; Klein, N.; Taylor-Robinson, D.
2017. Proceedings of the 32th International Workshop on Statistical Modelling. Ed.: M. Grzegorczyk. Vol. 1, 305–310, Statistical Modelling Society

Boosting distributional regression models for multivariate responses
Mayr, A.; Thomas, J.; Schmid, M.; Faschingbauer, F.; Klein, N.
2017. Proceedings of the 32th International Workshop on Statistical Modelling. Ed.: M. Grzegorczyk. Vol. 1, 97–102, Statistical Modelling Society

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, 145–154. doi:10.1016/j.mbs.2016.11.016

Scale-Dependent Priors for Variance Parameters in Structured Additive Distributional Regression
Klein, N.; Kneib, T.
2016. Bayesian Analysis, 11 (4), 1071–1106. doi:10.1214/15-BA983

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), 663–698. doi:10.1093/erae/jbv028

Modelling Hospital Admission and Length of Stay by Means of Generalised Count Data Models
Herwartz, H.; Klein, N.; Strumann, C.
2016. Journal of Applied Econometrics, 31 (6), 1159–1182. doi:10.1002/jae.2454

Simultaneous inference in structured additive conditional copula regression models: a unifying Bayesian approach
Klein, N.; Kneib, T.
2016. Statistics and Computing, 26 (4), 841–860. doi:10.1007/s11222-015-9573-6

Corridors restore animal-mediated pollination in fragmented tropical forest landscapes
Kormann, U.; Scherber, C.; Tscharntke, T.; Klein, N.; Larbig, M.; Valente, J. J.; Hadley, A. S.; Betts, M. G.
2016. Proceedings of the Royal Society B: Biological Sciences, 283 (1823), 20152347. doi:10.1098/rspb.2015.2347

Hedonic House Price Modeling Based on Multilevel Structured Additive Regression
Razen, A.; Brunauer, W.; Klein, N.; Lang, S.; Umlauf, N.
2015. Computational Approaches for Urban Environments. Ed.: M. Helbich, 97–122, Springer International Publishing. doi:10.1007/978-3-319-11469-9_5

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), 569–591. doi:10.1111/rssc.12090

Bayesian Generalized Additive Models for Location, Scale, and Shape for Zero-Inflated and Overdispersed Count Data
Klein, N.; Kneib, T.; Lang, S.
2015. Journal of the American Statistical Association, 110 (509), 405–419. doi:10.1080/01621459.2014.912955

A Semiparametric Analysis of Conditional Income Distributions
Sohn, A.; Klein, N.; Kneib, T.
2015. Journal of Contextual Economics – Schmollers Jahrbuch, 135 (1), 13–22. doi:10.3790/schm.135.1.13

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, 225–249. doi:10.1016/j.insmatheco.2014.02.001

Bivariate Gaussian Distributional Regression: An Application on Diabetes
Klein, N.; Gude, F.; Cadarso-Suárez, C.; Kneib, T.
2014. Proceedings of the 29th International Workshop on Statistical Modelling. Ed.: T. Kneib. Vol. 1, 167–172, Statistical Modelling Society

Bayesian Generalized Additive Models for Location, Scale and Shape for Insurance Data
Klein, N.; Kneib, T.; Lang, S.
2013. Proceedings of the 28th International Workshop on Statistical Modelling. Ed.: V.M.R. Muggeo. Vol. 2, 645–650, Statistical Modelling Society