Forschungsgruppe Methods for Big Data

In der Gruppe MBD beschäftigen wir uns mit einer Frage, die in der heutigen datengesteuerten Welt relevanter denn je ist: Wie können wir innovative, zuverlässige und generalisierbare Methoden entwickeln, um große Datenmengen zu verarbeiten und komplexe Probleme zu lösen?

Unsere Gruppe, unter der Leitung von Prof. Dr. Nadja Klein, ist auf Bayesianische Lernmethoden spezialisiert, ein leistungsfähiger Ansatz, der es uns ermöglicht, Vorwissen in Modelle einzubeziehen, Unsicherheiten zu quantifizieren und mehr Klarheit in die „Black Boxes“ des Machine Learnings zu bringen. So nutzen wir zum Beispiel die Erkenntnisse von Experten oder sparsitätsinduzierenden Mechanismen, um Modelle genauer, robuster und dateneffizienter zu machen. Indem wir die Präzision und Zuverlässigkeit der Bayesianischen Statistik mit der Anpassungsfähigkeit des Machine und Deep Learnings verbinden, wollen wir das Beste aus beiden Welten bieten.

Unsere Forschung umfasst theoretische Analysen, Methodenentwicklung und reale Anwendungen. So erarbeiten einige unserer Mitglieder neue Priors, andere entwickeln skalierbare und vertrauenswürdige Bayesian neural networks, und wieder andere verbessern die Erklärbarkeit komplexer Systeme. Auf der Anwendungsseite umfassen unsere Methoden verschiedene Bereiche - von der Analyse komplexer biomedizinischer Daten über die Vorhersage von Wettermustern bis hin zur Verbesserung autonomer Fahrtechnologien.

Weitere Details zu unseren Forschungsaktivitäten findet man auf unserer Website.

Weitere Informationen zum Forschungsteam, zu ausgeschriebenen HiWi-Stellen und Masterarbeiten findet man unter Organisation Methods for Big Data.

Publikationsliste

2025

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

2024

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

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

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

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

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

Truly Multivariate Structured Additive Distributional Regression
Kock, L.; Klein, N.
2024. Journal of Computational and Graphical Statistics, 1–17. doi:10.1080/10618600.2024.2434181

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

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

Calibrated Multivariate Regression with Localized PIT Mappings
Kock, L.; Rodrigues, G. S.; Sisson, S. A.; Klein, N.; Nott, D. J.
2024

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

2023

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

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

2022

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

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

2021

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

2020

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

2019

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

2018

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

2017

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

2016

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

2015

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

2014

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

2013

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