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2020 – today
- 2024
[j35]Gillian Kant, Ivan Zhelyazkov, Anton Thielmann, Christoph Weisser, Michael Schlee, Christoph Ehrling, Benjamin Säfken, Thomas Kneib:
One-way ticket to the moon? An NLP-based insight on the phenomenon of small-scale neo-broker trading. Soc. Netw. Anal. Min. 14(1): 121 (2024)- [j34]Anton Frederik Thielmann, Arik Reuter, Thomas Kneib, David Rügamer, Benjamin Säfken:
Interpretable Additive Tabular Transformer Networks. Trans. Mach. Learn. Res. 2024 (2024) - [c6]Anton Frederik Thielmann, René-Marcel Kruse, Thomas Kneib, Benjamin Säfken:
Neural Additive Models for Location Scale and Shape: A Framework for Interpretable Neural Regression Beyond the Mean. AISTATS 2024: 1783-1791 - [c5]Jonathan Henrich, Christian Post, Thomas Kneib, Ramin Yahyapour, Sven Bingert, Imke Traulsen:
Entwicklung eines vielfältigen und anspruchsvollen Benchmark-Datensatzes für die Detektion von Schweinen in Bildern. GIL 2024: 293-298 - [i7]Arik Reuter, Anton Thielmann, Christoph Weisser, Benjamin Säfken, Thomas Kneib:
Probabilistic Topic Modelling with Transformer Representations. CoRR abs/2403.03737 (2024) - [i6]Parastoo Semnani, Mihail Bogojeski, Florian Bley, Zizheng Zhang, Qiong Wu, Thomas Kneib, Jan Herrmann, Christoph Weisser, Florina Patcas, Klaus-Robert Müller:
A Machine Learning and Explainable AI Framework Tailored for Unbalanced Experimental Catalyst Discovery. CoRR abs/2407.18935 (2024) - 2023
- [j33]Rouven Schmidt
, Thomas Kneib:
Multivariate distributional stochastic frontier models. Comput. Stat. Data Anal. 187: 107796 (2023) - [j32]Christoph Weisser, Christoph Gerloff, Anton Thielmann, Andre Python, Arik Reuter, Thomas Kneib, Benjamin Säfken:
Pseudo-document simulation for comparing LDA, GSDMM and GPM topic models on short and sparse text using Twitter data. Comput. Stat. 38(2): 647-674 (2023) - [j31]David Rügamer, Philipp F. M. Baumann, Thomas Kneib, Torsten Hothorn:
Probabilistic time series forecasts with autoregressive transformation models. Stat. Comput. 33(2): 37 (2023) - [j30]Anja Rappl, Thomas Kneib, Stefan Lang, Elisabeth Bergherr:
Spatial joint models through Bayesian structured piecewise additive joint modelling for longitudinal and time-to-event data. Stat. Comput. 33(6): 135 (2023) - [j29]Fernando Marmolejo-Ramos, Mauricio Tejo, Marek Brabec, Jakub Kuzilek, Srecko Joksimovic, Vitomir Kovanovic, Jorge González, Thomas Kneib, Peter Bühlmann, Lucas Kook, Guillermo Briseño-Sánchez, Raydonal Ospina:
Distributional regression modeling via generalized additive models for location, scale, and shape: An overview through a data set from learning analytics. WIREs Data Mining Knowl. Discov. 13(1) (2023) - [c4]Anton Thielmann, Christoph Weisser, Thomas Kneib, Benjamin Säfken:
Coherence based Document Clustering. ICSC 2023: 9-16 - [i5]Anton Thielmann, René-Marcel Kruse, Thomas Kneib, Benjamin Säfken:
Neural Additive Models for Location Scale and Shape: A Framework for Interpretable Neural Regression Beyond the Mean. CoRR abs/2301.11862 (2023) - [i4]Jonathan Henrich, Jan van Delden, Dominik Seidel, Thomas Kneib, Alexander S. Ecker:
TreeLearn: A Comprehensive Deep Learning Method for Segmenting Individual Trees from Forest Point Clouds. CoRR abs/2309.08471 (2023) - 2022
- [j28]Paul F. V. Wiemann, Nadja Klein, Thomas Kneib:
Correcting for sample selection bias in Bayesian distributional regression models. Comput. Stat. Data Anal. 168: 107382 (2022) - [j27]Isa Marques, Thomas Kneib, Nadja Klein:
A non-stationary model for spatially dependent circular response data based on wrapped Gaussian processes. Stat. Comput. 32(5): 73 (2022) - [c3]Arne Tillmann, Lindrit Kqiku, Delphine Reinhardt, Christoph Weisser, Benjamin Säfken, Thomas Kneib:
Privacy Estimation on Twitter: Modelling the Effect of Latent Topics on Privacy by Integrating XGBoost, Topic and Generalized Additive Models. SmartWorld/UIC/ScalCom/DigitalTwin/PriComp/Meta 2022: 2325-2332 - [i3]Alexander März, Thomas Kneib:
Distributional Gradient Boosting Machines. CoRR abs/2204.00778 (2022) - [i2]Andreas Buchmüller, Gillian Kant, Christoph Weisser, Benjamin Säfken, Krisztina Kis-Katos, Thomas Kneib:
Twitmo: A Twitter Data Topic Modeling and Visualization Package for R. CoRR abs/2207.11236 (2022) - 2021
- [j26]Arne Tillmann, Anton Thielmann, Gillian Kant, Christoph Weisser, Benjamin Säfken, Alexander Silbersdorff, Thomas Kneib:
AuDoLab: Automatic document labelling and classification for extremely unbalanced data. J. Open Source Softw. 6(66): 3719 (2021) - [j25]Benjamin Säfken, David Rügamer, Thomas Kneib, Sonja Greven:
Conditional Model Selection in Mixed-Effects Models with cAIC4. J. Stat. Softw. 99(8) (2021) - [c2]Mattias Luber, Christoph Weisser, Benjamin Säfken, Alexander Silbersdorff, Thomas Kneib, Krisztina Kis-Katos:
Identifying Topical Shifts in Twitter Streams: An Integration of Non-negative Matrix Factorisation, Sentiment Analysis and Structural Break Models for Large Scale Data. MISDOOM 2021: 33-49 - [i1]David Rügamer, Philipp F. M. Baumann, Thomas Kneib, Torsten Hothorn:
Transforming Autoregression: Interpretable and Expressive Time Series Forecast. CoRR abs/2110.08248 (2021) - 2020
- [j24]Bruno Santos, Thomas Kneib:
Noncrossing structured additive multiple-output Bayesian quantile regression models. Stat. Comput. 30(4): 855-869 (2020) - [j23]Hendrik van der Wurp, Andreas Groll, Thomas Kneib, Giampiero Marra, Rosalba Radice:
Generalised joint regression for count data: a penalty extension for competitive settings. Stat. Comput. 30(5): 1419-1432 (2020) - [c1]Jan Röder, Jan Muntermann, Thomas Kneib:
Towards a Taxonomy of Data Heterogeneity. Wirtschaftsinformatik (Zentrale Tracks) 2020: 293-308
2010 – 2019
- 2019
- [j22]Hauke Thaden, Nadja Klein, Thomas Kneib:
Multivariate effect priors in bivariate semiparametric recursive Gaussian models. Comput. Stat. Data Anal. 137: 51-66 (2019) - [j21]Andreas Groll, Julien Hambuckers, Thomas Kneib, Nikolaus Umlauf:
LASSO-type penalization in the framework of generalized additive models for location, scale and shape. Comput. Stat. Data Anal. 140: 59-73 (2019) - [j20]Elmar Spiegel, Thomas Kneib, Fabian Otto-Sobotka:
Generalized additive models with flexible response functions. Stat. Comput. 29(1): 123-138 (2019) - 2018
- [j19]Laura Ríos-Pena, Thomas Kneib, Carmen Cadarso-Suárez, Nadja Klein, Manuel Marey-Pérez:
Studying the occurrence and burnt area of wildfires using zero-one-inflated structured additive beta regression. Environ. Model. Softw. 110: 107-118 (2018) - [j18]Bernhard Baumgartner, Daniel Guhl, Thomas Kneib, Winfried J. Steiner:
Flexible estimation of time-varying effects for frequently purchased retail goods: a modeling approach based on household panel data. OR Spectr. 40(4): 837-873 (2018) - 2017
- [j17]Stefanie Friedrichs, Juliane Manitz, Patricia Burger, Christopher I. Amos, Angela Risch, Jenny Chang-Claude, Heinz-Erich Wichmann, Thomas Kneib, Heike Bickeböller, Benjamin Hofner:
Pathway-Based Kernel Boosting for the Analysis of Genome-Wide Association Studies. Comput. Math. Methods Medicine 2017: 6742763:1-6742763:17 (2017) - [j16]Roland Langrock, Thomas Kneib, Richard Glennie, Théo Michelot:
Markov-switching generalized additive models. Stat. Comput. 27(1): 259-270 (2017) - [j15]Elisabeth Waldmann, Fabian Sobotka, Thomas Kneib:
Bayesian regularisation in geoadditive expectile regression. Stat. Comput. 27(6): 1539-1553 (2017) - 2016
- [j14]Benjamin Hofner, Thomas Kneib, Torsten Hothorn:
A unified framework of constrained regression. Stat. Comput. 26(1-2): 1-14 (2016) - [j13]Nadja Klein, Thomas Kneib:
Simultaneous inference in structured additive conditional copula regression models: a unifying Bayesian approach. Stat. Comput. 26(4): 841-860 (2016) - 2015
- [j12]Roland Langrock, Théo Michelot, Alexander Sohn, Thomas Kneib:
Semiparametric stochastic volatility modelling using penalized splines. Comput. Stat. 30(2): 517-537 (2015) - [j11]María Xosé Rodríguez-Álvarez, Dae-Jin Lee, Thomas Kneib, María Durbán, Paul H. C. Eilers:
Fast smoothing parameter separation in multidimensional generalized P-splines: the SAP algorithm. Stat. Comput. 25(5): 941-957 (2015) - [j10]Elisabeth Waldmann, Thomas Kneib:
Variational approximations in geoadditive latent Gaussian regression: mean and quantile regression. Stat. Comput. 25(6): 1247-1263 (2015) - 2014
- [j9]Stefan Lang, Nikolaus Umlauf, Peter Wechselberger, Kenneth Harttgen, Thomas Kneib:
Multilevel structured additive regression. Stat. Comput. 24(2): 223-238 (2014) - 2013
- [j8]Benjamin Hofner, Torsten Hothorn, Thomas Kneib:
Variable selection and model choice in structured survival models. Comput. Stat. 28(3): 1079-1101 (2013) - [j7]Fabian Sobotka, Göran Kauermann, Linda Schulze Waltrup, Thomas Kneib:
On confidence intervals for semiparametric expectile regression. Stat. Comput. 23(2): 135-148 (2013) - 2012
- [j6]Fabian Sobotka, Thomas Kneib:
Geoadditive expectile regression. Comput. Stat. Data Anal. 56(4): 755-767 (2012) - 2010
- [j5]Torsten Hothorn, Peter Bühlmann, Thomas Kneib, Matthias Schmid, Benjamin Hofner:
Model-based Boosting 2.0. J. Mach. Learn. Res. 11: 2109-2113 (2010) - [j4]Ludwig Fahrmeir, Thomas Kneib, Susanne Konrath:
Bayesian regularisation in structured additive regression: a unifying perspective on shrinkage, smoothing and predictor selection. Stat. Comput. 20(2): 203-219 (2010)
2000 – 2009
- 2009
- [j3]Fabian Scheipl, Thomas Kneib:
Locally adaptive Bayesian P-splines with a Normal-Exponential-Gamma prior. Comput. Stat. Data Anal. 53(10): 3533-3552 (2009) - 2008
- [j2]Carolin Strobl, Anne-Laure Boulesteix, Thomas Kneib, Thomas Augustin, Achim Zeileis:
Conditional variable importance for random forests. BMC Bioinform. 9 (2008) - 2006
- [j1]Thomas Kneib:
Mixed model-based inference in geoadditive hazard regression for interval-censored survival times. Comput. Stat. Data Anal. 51(2): 777-792 (2006)
Coauthor Index
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