Publication list

Limit Theorems for Stochastic Differential Equations and Their Applications to Statistical Inference under Multiscale Observations. PhD dissertation
Borodavka, J. I.
2026, August 13. Karlsruher Institut für Technologie (KIT). doi:10.5445/IR/1000196151

Random irregular histograms
Simensen, O. H.; Christensen, D.; Hjort, N. L.
2026. Computational Statistics & Data Analysis, 220, 108367. doi:10.1016/j.csda.2026.108367

Effects of Appearance Preoccupation and Safety Behaviors in Body Dysmorphic Disorder: An Ecological Momentary Assessment Study
Hohensee, N.; Dietel, F. A.; Schulte, J.; Doebler, P.; Klein, N.; Buhlmann, U.
2026. Behavior Therapy, 57 (4), 667–680. doi:10.1016/j.beth.2026.01.003

Performance evaluation of mixed-precision Runge–Kutta methods for the solution of partial differential equations
Dravins, I.; Koch, M.; Griehl, V.; Kormann, K.
2026. The International Journal of High Performance Computing Applications, 40 (4), 487–507. doi:10.1177/10943420251392963

Enhanced Variable Selection for Boosting Sparser and Less Complex Models in Distributional Copula Regression
Strömer, A.; Klein, N.; Staerk, C.; Faschingbauer, F.; Klinkhammer, H.; Mayr, A.
2026. Statistics in Biosciences, 18 (2), 372–394. doi:10.1007/s12561-025-09491-8

Stochastic Weather Generation for Scenario‐Neutral Impact Assessments Using Simulation‐Based Inference
Groenke, B. R.; Wessel, J.; Miersch, P.; Klein, N.; Zscheischler, J.
2026. Journal of Geophysical Research: Machine Learning and Computation, 3 (2), e2025JH000902. doi:10.1029/2025JH000902

Direct optimization of the probability of lesion origin in proton treatment planning for low‐grade glioma patients
Ortkamp, T.; Sallem, H.; Harrabi, S.; Frank, M.; Jäkel, O.; Bauer, J.; Wahl, N.
2026. Medical Physics, 53 (4), Art.-Nr.: e70395. doi:10.1002/mp.70395

Scalable Learning of Multivariate Distributions via Coresets
Ding, Z.; Ickstadt, K.; Klein, N.; Munteanu, A.; Omlor, S.
2026. Proceedings of the 29th International Conference on Artificial Intelligence and Statistics; Tanger, Marokko, 02.05.2026

Bivariate distributional copula regression for mixed non-time-to-event and time-to-event responses
Sanchez, G. B.; Groll, A.
2026. Statistical Modelling. doi:10.1177/1471082X251401632

Investigating Matrix Repartitioning to Address the Over and Undersubscription Challenge for a GPU-Based CFD Solver
Olenik, G.; Koch, M.; Anzt, H.
2026. High Performance Computing – ISC High Performance 2025 International Workshops, Hamburg, Germany, June 10–13, 2025, Revised Selected Papers. Ed.: S. Neuwirth, 468–479, Springer Nature Switzerland. doi:10.1007/978-3-032-07612-0_36

Calibrating Multivariate Regression with Localized PIT Mappings
Kock, L.; Rodrigues, G. S.; Sisson, S. A.; Klein, N.; Nott, D. J.
2026. Journal of computational and graphical statistics, 1–22. doi:10.1080/10618600.2026.2652919

Contributed Discussion on “Model Uncertainty and Missing Data: An Objective Bayesian Perspective”
Klein, N.; Bianco, N.
2025, December 1. International Society for Bayesian Analysis (ISBA). doi:10.1214/25-BA1531

Deep Mixture of Linear Mixed Models for Complex Longitudinal Data
Kock, L.; Klein, N.; Nott, D. J.
2025. Statistics in Medicine, 44 (23-24), Art.-Nr.: e70288. doi:10.1002/sim.70288

Dynamics of Insight, Emotion Regulation, and Emotional Clarity in Obsessive–Compulsive Disorder
Bischof, C.; Hohensee, N.; Dietel, F. A.; Doebler, P.; Klein, N.; Buhlmann, U.
2025. Cognitive Therapy and Research, 49 (5), 934–944. doi:10.1007/s10608-025-10595-0

2D implementation of Kinetic-diffusion Monte Carlo in Eiron
Lappi, O.; Løvbak, E.; Steel, T.; Samaey, G.
2025. arxiv. doi:10.48550/arXiv.2509.19140

AutoHist.jl: A Julia package for fast and automatic histogram construction
Simensen, O. H.
2025. Journal of Open Source Software, 10 (113), Art.Nr: 8850. doi:10.21105/joss.08850

Numerical analysis of fluid estimation for source terms in neutral particles simulation
Tang, Z.; Løvbak, E.; Koellermeier, J.; Samaey, G.
2025. arxiv. doi:10.48550/arXiv.2509.11883

An Investigation into the Distribution of Ratios of Particle Solver-based Likelihoods
Løvbak, E.; Krumscheid, S.
2025. arxiv. doi:10.48550/arXiv.2508.05303

Uncertainty-Aware Trajectory Prediction via Rule-Regularized Heteroscedastic Deep Classification
Manas, K.; Schlauch, C.; Paschke, A.; Wirth, C.; Klein, N.
2025. Proceedings of the Robotics: Science and Systems (RSS), Paper ID 144

Entwicklung von computergestützten Lehr- und Lernmaterialien für einen realitätsbezogenen Mathematikunterricht zum Kontext Sprachverarbeitung. PhD dissertation
Hofmann, S.
2025, June 5. Karlsruher Institut für Technologie (KIT). doi:10.5445/IR/1000182142

Building bridges between Bayesian Statistics and Deep Learning
Klein, N.; Kassem Sbeyti, M.
2025. Mitteilungen der Deutschen Mathematiker-Vereinigung, 33 (2), 102–105. doi:10.1515/dmvm-2025-0033

Towards a platform-portable linear algebra backend for OpenFOAM
Olenik, G.; Koch, M.; Boutanios, Z.; Anzt, H.
2025. Meccanica, 60 (6), 1659–1672. doi:10.1007/s11012-024-01806-1

Wissenschaftliche Tools in der Hochschullehre mit bwJupyter
Hofmann, S.; Hörter, J.
2025, May 7

Boosting distributional copula regression for bivariate binary, discrete and mixed responses
Sanchez, G. B.; Klein, N.; Klinkhammer, H.; Mayr, A.
2025. Statistical Methods in Medical Research, 34 (5), 887–902. doi:10.1177/09622802241313294

Modeling the ratio of correlated biomarkers using copula regression
Berger, M.; Klein, N.; Wagner, M.; Schmid, M.
2025. Statistical Methods in Medical Research, 34 (5), 968–985. doi:10.1177/09622802241313293

Scalable Estimation for Structured Additive Distributional Regression
Umlauf, N.; Seiler, J.; Wetscher, M.; Simon, T.; Lang, S.; Klein, N.
2025. Journal of Computational and Graphical Statistics, 34 (2), 601–617. doi:10.1080/10618600.2024.2388604

Density regression via Dirichlet process mixtures of normal structured additive regression models
Rodríguez-Álvarez, M. X.; Inácio, V.; Klein, N.
2025. Statistics and Computing, 35 (2), 47. doi:10.1007/s11222-025-10567-0

Anisotropic multidimensional smoothing using Bayesian tensor product P-splines
Bach, P.; Klein, N.
2025. Statistics and Computing, 35 (2), Art.-Nr.: 43. doi:10.1007/s11222-025-10569-y

Asymptotic-Preserving and Energy Stable Dynamical Low-Rank Approximation for Thermal Radiative Transfer Equations
Frank, M.; Kusch, J.; Patwardhan, C.
2025. Multiscale Modeling & Simulation, 23 (1), 278–312. doi:10.1137/24M1646303

Associations Among in‐The‐Moment Emotional Clarity, Emotion Regulation, and Psychopathology in Obsessive‐Compulsive Disorder
Hohensee, N.; Bischof, C.; Dietel, F. A.; Klein, N.; Doebler, P.; Buhlmann, U.
2025. Depression and Anxiety, 2025 (1). doi:10.1155/da/7799020

Boosting Causal Additive Models
Kertel, M.; Klein, N.
2025. Journal of Machine Learning Research, 26 (169)

Der Modellansatz: Modell253 - Bayesian Learning
Kassem Sbeyti, M.; Klein, N.
2025

Identifying the determinants of lifelong singlehood via boosted mixture cure models
Sanchez, G. B.; Carollo, A.; Klein, N.
2025. Proceedings of the 39th International Workshop on Statistical Modelling. Ed.: D. O’Sullivan, 104–109

Solar Wind Speed Forecasting From Solar Images Using Distributional Regression
Collin, D.; Shprits, Y.; Hofmeister, S.; Bianco, S.; Klein, N.; Gallego, G.
2025. EGU General Assembly 2025. Abstract, 1 S., Copernicus. doi:10.5194/egusphere-egu25-11587

Posterior Concentration Rates for Bayesian Penalized Splines
Bach, P.; Klein, N.
2025. Bayesian Analysis, -1 (-1). doi:10.1214/25-BA1523

Building Blocks for Robust and Effective Semi-Supervised Real-World Object Detection
Kassem Sbeyti, M.; Klein, N.; Nowzad, A.; Sivrikaya, F.; Albayrak, S.
2025. Transactions on Machine Learning Research, 3, 1–26

WeedsGalore: A Multispectral and Multitemporal UAV-Based Dataset for Crop and Weed Segmentation in Agricultural Maize Fields
Celikkan, E.; Kunzmann, T.; Yeskaliyev, Y.; Itzerott, S.; Klein, N.; Herold, M.
2025. 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), Tucson, AZ, USA, 26 February 2025 - 06 March 2025, 4767–4777, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/WACV61041.2025.00467

Bayesian Regularized Regression Copula Processes for Multivariate Responses
Klein, N.; Smith, M. S.; Chisholm, R. A.; Nott, D. J.
2025. Journal of Computational and Graphical Statistics, 34 (4), 1466–1479. doi:10.1080/10618600.2025.2458504

Low-rank variance reduction for uncertain radiative transfer with control variates
Patwardhan, C.; Stammer, P.; Løvbak, E.; Kusch, J.; Krumscheid, S.
2025. Karlsruher Institut für Technologie (KIT). doi:10.5445/IR/1000178192

Associations between Emotion Regulation, Symptom Severity, and Affect in Obsessive-Compulsive Disorder
Hohensee, N.; Bischof, C.; Dietel, F. A.; Klein, N.; Doebler, P.; Buhlmann, U.
2025. Journal of Obsessive-Compulsive and Related Disorders, 44, Article no: 100934. doi:10.1016/j.jocrd.2024.100934

Truly Multivariate Structured Additive Distributional Regression
Kock, L.; Klein, N.
2025. Journal of Computational and Graphical Statistics, 34 (4), 1189–1201. doi:10.1080/10618600.2024.2434181

Solving stationary nonlinear Fokker-Planck equations via sampling
Li, L.; Tang, Y.; Zhang, J.
2025. SIAM journal on applied mathematics, 85 (1), 249–277. doi:10.1137/23M1616054

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

Quantifying agricultural land-use intensity for spatial biodiversity modelling: implications of different metrics and spatial aggregation methods
Roilo, S.; Paulus, A.; Alarcón-Segura, V.; Kock, L.; Beckmann, M.; Klein, N.; Cord, A. F.
2024. Landscape Ecology, 39 (3), Art.-Nr.: 55. doi:10.1007/s10980-024-01853-9

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, Germany, April 8–10, 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, Ed.) 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

The Deep Promotion Time Cure Model
Medina-Olivares, V.; Lessmann, S.; Klein, N.
2024. IEEE Transactions on Neural Networks and Learning Systems, 35 (12), 18848–18858. doi:10.1109/TNNLS.2024.3398559

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. doi:10.1109/CVPRW63382.2024.00358

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. Proceedings of Machine Learning Research, 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

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

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

Learning Causal Graphs in Manufacturing Domains Using Structural Equation Models
Kertel, M.; Harmeling, S.; Pauly, M.; Klein, N.
2023. International Journal of Semantic Computing, 17 (04), 511–528. doi:10.1142/S1793351X23630023

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

Farm structure and environmental context drive farmers’ decisions on the spatial distribution of ecological focus areas in Germany
Alarcón-Segura, V.; Roilo, S.; Paulus, A.; Beckmann, M.; Klein, N.; Cord, A. F.
2023. Landscape Ecology, 38 (9), 2293–2305. doi:10.1007/s10980-023-01709-8

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 (Ed.), 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, Ed.) 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. (Eds.)
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

Statistical model building: Background “knowledge” based on inappropriate preselection causes misspecification
Hafermann, L.; Becher, H.; Herrmann, C.; Klein, N.; Heinze, G.; Rauch, G.
2021. BMC Medical Research Methodology, 21 (1), 196. doi:10.1186/s12874-021-01373-z

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, Ed.) 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, Ed.) 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