Forschungsgruppe Methods for Big Data

Die Forschungsgruppe Methods for Big Data bei einem Retreat im Pfälzer Wald
Die Forschungsgruppe Methods for Big Data bei einem Retreat im Pfälzer Wald

Die Forschungsinteressen der Gruppe Methods for Big Data unter der Leitung von Prof. Dr. Nadja Klein liegen im Wesentlichen an der Schnittstelle zwischen maschinellem Lernen und traditionellen statistischen Methoden. Nachfolgend eine Auswahl von Themen, an denen die Forschungsgruppe arbeitet: KleinLab.

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

Publikationsliste


2024
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
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