Mixed model-based inference in geoadditive hazard regression for interval-censored survival times. - Thomas Kneib (Comput. Stat. Data Anal., 2006)

Conditional variable importance for random forests. - Carolin Strobl; Anne-Laure Boulesteix; Thomas Kneib; Thomas Augustin 001; Achim Zeileis (BMC Bioinform., 2008)

Locally adaptive Bayesian P-splines with a Normal-Exponential-Gamma prior. - Fabian Scheipl; Thomas Kneib (Comput. Stat. Data Anal., 2009)

Model-based Boosting 2.0. - Torsten Hothorn; Peter Bühlmann; Thomas Kneib; Matthias Schmid; Benjamin Hofner (J. Mach. Learn. Res., 2010)

Bayesian regularisation in structured additive regression: a unifying perspective on shrinkage, smoothing and predictor selection. - Ludwig Fahrmeir; Thomas Kneib; Susanne Konrath (Stat. Comput., 2010)

Geoadditive expectile regression. - Fabian Sobotka; Thomas Kneib (Comput. Stat. Data Anal., 2012)

Variable selection and model choice in structured survival models. - Benjamin Hofner; Torsten Hothorn; Thomas Kneib (Comput. Stat., 2013)

On confidence intervals for semiparametric expectile regression. - Fabian Sobotka; Göran Kauermann; Linda Schulze Waltrup; Thomas Kneib (Stat. Comput., 2013)

Multilevel structured additive regression. - Stefan Lang 003; Nikolaus Umlauf; Peter Wechselberger; Kenneth Harttgen; Thomas Kneib (Stat. Comput., 2014)

Semiparametric stochastic volatility modelling using penalized splines. - Roland Langrock; Théo Michelot; Alexander Sohn; Thomas Kneib (Comput. Stat., 2015)

Fast smoothing parameter separation in multidimensional generalized P-splines: the SAP algorithm. - María Xosé Rodríguez-Álvarez; Dae-Jin Lee; Thomas Kneib; María Durbán; Paul H. C. Eilers (Stat. Comput., 2015)

Variational approximations in geoadditive latent Gaussian regression: mean and quantile regression. - Elisabeth Waldmann; Thomas Kneib (Stat. Comput., 2015)

A unified framework of constrained regression. - Benjamin Hofner; Thomas Kneib; Torsten Hothorn (Stat. Comput., 2016)

Simultaneous inference in structured additive conditional copula regression models: a unifying Bayesian approach. - Nadja Klein; Thomas Kneib (Stat. Comput., 2016)

Pathway-Based Kernel Boosting for the Analysis of Genome-Wide Association Studies. - Stefanie Friedrichs; Juliane Manitz; Patricia Burger; Christopher I. Amos; Angela Risch; Jenny Chang-Claude; Heinz-Erich Wichmann; Thomas Kneib; Heike Bickeböller; Benjamin Hofner (Comput. Math. Methods Medicine, 2017)

Markov-switching generalized additive models. - Roland Langrock; Thomas Kneib; Richard Glennie; Théo Michelot (Stat. Comput., 2017)

Bayesian regularisation in geoadditive expectile regression. - Elisabeth Waldmann; Fabian Sobotka; Thomas Kneib (Stat. Comput., 2017)

Studying the occurrence and burnt area of wildfires using zero-one-inflated structured additive beta regression. - Laura Ríos-Pena; Thomas Kneib; Carmen Cadarso-Suárez; Nadja Klein; Manuel Marey-Pérez (Environ. Model. Softw., 2018)

Flexible estimation of time-varying effects for frequently purchased retail goods: a modeling approach based on household panel data. - Bernhard Baumgartner; Daniel Guhl; Thomas Kneib; Winfried J. Steiner (OR Spectr., 2018)

LASSO-type penalization in the framework of generalized additive models for location, scale and shape. - Andreas Groll; Julien Hambuckers; Thomas Kneib; Nikolaus Umlauf (Comput. Stat. Data Anal., 2019)

Multivariate effect priors in bivariate semiparametric recursive Gaussian models. - Hauke Thaden; Nadja Klein; Thomas Kneib (Comput. Stat. Data Anal., 2019)

Generalized additive models with flexible response functions. - Elmar Spiegel; Thomas Kneib; Fabian Otto-Sobotka (Stat. Comput., 2019)

Towards a Taxonomy of Data Heterogeneity. - Jan Röder; Jan Muntermann; Thomas Kneib (Wirtschaftsinformatik (Zentrale Tracks), 2020)

Noncrossing structured additive multiple-output Bayesian quantile regression models. - Bruno Santos 002; Thomas Kneib (Stat. Comput., 2020)

Generalised joint regression for count data: a penalty extension for competitive settings. - Hendrik van der Wurp; Andreas Groll; Thomas Kneib; Giampiero Marra; Rosalba Radice (Stat. Comput., 2020)

Identifying Topical Shifts in Twitter Streams: An Integration of Non-negative Matrix Factorisation, Sentiment Analysis and Structural Break Models for Large Scale Data. - Mattias Luber; Christoph Weisser; Benjamin Säfken; Alexander Silbersdorff; Thomas Kneib; Krisztina Kis-Katos (MISDOOM, 2021)

Transforming Autoregression: Interpretable and Expressive Time Series Forecast. - David Rügamer; Philipp F. M. Baumann; Thomas Kneib; Torsten Hothorn (CoRR, 2021)

AuDoLab: Automatic document labelling and classification for extremely unbalanced data. - Arne Tillmann; Anton Thielmann; Gillian Kant; Christoph Weisser; Benjamin Säfken; Alexander Silbersdorff; Thomas Kneib (J. Open Source Softw., 2021)

Conditional Model Selection in Mixed-Effects Models with cAIC4. - Benjamin Säfken; David Rügamer; Thomas Kneib; Sonja Greven (J. Stat. Softw., 2021)

Privacy Estimation on Twitter: Modelling the Effect of Latent Topics on Privacy by Integrating XGBoost, Topic and Generalized Additive Models. - Arne Tillmann; Lindrit Kqiku; Delphine Reinhardt; Christoph Weisser; Benjamin Säfken; Thomas Kneib (SmartWorld/UIC/ScalCom/DigitalTwin/PriComp/Meta, 2022)

Distributional Gradient Boosting Machines. - Alexander März; Thomas Kneib (CoRR, 2022)

Twitmo: A Twitter Data Topic Modeling and Visualization Package for R. - Andreas Buchmüller; Gillian Kant; Christoph Weisser; Benjamin Säfken; Krisztina Kis-Katos; Thomas Kneib (CoRR, 2022)

Correcting for sample selection bias in Bayesian distributional regression models. - Paul F. V. Wiemann; Nadja Klein; Thomas Kneib (Comput. Stat. Data Anal., 2022)

A non-stationary model for spatially dependent circular response data based on wrapped Gaussian processes. - Isa Marques; Thomas Kneib; Nadja Klein (Stat. Comput., 2022)

Coherence based Document Clustering. - Anton Thielmann; Christoph Weisser; Thomas Kneib; Benjamin Säfken (ICSC, 2023)

TreeLearn: A Comprehensive Deep Learning Method for Segmenting Individual Trees from Forest Point Clouds. - Jonathan Henrich; Jan van Delden; Dominik Seidel; Thomas Kneib; Alexander S. Ecker (CoRR, 2023)

Distributional regression modeling via generalized additive models for location, scale, and shape: An overview through a data set from learning analytics. - 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 (WIREs Data Mining Knowl. Discov., 2023)

Probabilistic time series forecasts with autoregressive transformation models. - David Rügamer; Philipp F. M. Baumann; Thomas Kneib; Torsten Hothorn (Stat. Comput., 2023)

Pseudo-document simulation for comparing LDA, GSDMM and GPM topic models on short and sparse text using Twitter data. - Christoph Weisser; Christoph Gerloff; Anton Thielmann; Andre Python; Arik Reuter; Thomas Kneib; Benjamin Säfken (Comput. Stat., 2023)

Multivariate distributional stochastic frontier models. - Rouven Schmidt; Thomas Kneib (Comput. Stat. Data Anal., 2023)

Spatial joint models through Bayesian structured piecewise additive joint modelling for longitudinal and time-to-event data. - Anja Rappl; Thomas Kneib; Stefan Lang 003; Elisabeth Bergherr (Stat. Comput., 2023)

Neural Additive Models for Location Scale and Shape: A Framework for Interpretable Neural Regression Beyond the Mean. - Anton Frederik Thielmann; René-Marcel Kruse; Thomas Kneib; Benjamin Säfken (AISTATS, 2024)

Entwicklung eines vielfältigen und anspruchsvollen Benchmark-Datensatzes für die Detektion von Schweinen in Bildern. - Jonathan Henrich; Christian Post; Thomas Kneib; Ramin Yahyapour; Sven Bingert; Imke Traulsen (GIL, 2024)

Probabilistic Topic Modelling with Transformer Representations. - Arik Reuter; Anton Thielmann; Christoph Weisser; Benjamin Säfken; Thomas Kneib (CoRR, 2024)

A Machine Learning and Explainable AI Framework Tailored for Unbalanced Experimental Catalyst Discovery. - Parastoo Semnani; Mihail Bogojeski; Florian Bley; Zizheng Zhang; Qiong Wu; Thomas Kneib; Jan Herrmann; Christoph Weisser; Florina Patcas; Klaus-Robert Müller (CoRR, 2024)

TreeLearn: A deep learning method for segmenting individual trees from ground-based LiDAR forest point clouds. - Jonathan Henrich; Jan van Delden; Dominik Seidel; Thomas Kneib; Alexander S. Ecker (Ecol. Informatics, 2024)

Interpretable Additive Tabular Transformer Networks. - Anton Frederik Thielmann; Arik Reuter; Thomas Kneib; David Rügamer; Benjamin Säfken (Trans. Mach. Learn. Res., 2024)

One-way ticket to the moon? An NLP-based insight on the phenomenon of small-scale neo-broker trading. - Gillian Kant; Ivan Zhelyazkov; Anton Thielmann; Christoph Weisser; Michael Schlee; Christoph Ehrling; Benjamin Säfken; Thomas Kneib (Soc. Netw. Anal. Min., 2024)

Hybrid Bernstein Normalizing Flows for Flexible Multivariate Density Regression with Interpretable Marginals. - Marcel Arpogaus; Thomas Kneib; Thomas Nagler; David Rügamer (UAI, 2025)

Graphical Transformation Models. - Matthias Herp; Johannes Brachem; Michael Altenbuchinger; Thomas Kneib (CoRR, 2025)

Benchmarking pig detection and tracking under diverse and challenging conditions. - Jonathan Henrich; Christian Post; Maximilian Zilke; Parth Shiroya; Emma Chanut; Amir Mollazadeh Yamchi; Ramin Yahyapour; Thomas Kneib; Imke Traulsen (CoRR, 2025)

Decoding synthetic news: an interpretable multimodal framework for the classification of news articles in a novel news corpus. - Michael Schlee; Gillian Kant; Christoph Ehrling; Benjamin Säfken; Thomas Kneib (Artif. Intell. Rev., 2025)