Addressing cluster-constant covariates in mixed effects models via likelihood-based boosting techniques von Griesbach, Colin, Groll, Andreas, Bergherr, Elisabeth

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Spatial joint models through Bayesian structured piecewise additive joint modelling for longitudinal and time-to-event data von Rappl, Anja, Kneib, Thomas, Lang, Stefan, Bergherr, Elisabeth

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Topics in the Haystack: Enhancing Topic Quality through Corpus Expansion von Thielmann, Anton, Reuter, Arik, Seifert, Quentin, Bergherr, Elisabeth, Säfken, Benjamin

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Variable Selection and Allocation in Joint Models via Gradient Boosting Techniques von Griesbach, Colin, Mayr, Andreas, Bergherr, Elisabeth

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Joint Modelling Approaches to Survival Analysis via Likelihood-Based Boosting Techniques von Griesbach, Colin, Groll, Andreas, Bergherr, Elisabeth

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Adaptive step-length selection in gradient boosting for Gaussian location and scale models von Zhang, Boyao, Hepp, Tobias, Greven, Sonja, Bergherr, Elisabeth

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Bayesian effect selection in structured additive quantile regression von Rappl, Anja, Carlan, Manuel, Kneib, Thomas, Klokman, Sebastiaan, Bergherr, Elisabeth

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Additive Mixed Models for Location, Scale and Shape via Gradient Boosting Techniques von Griesbach, Colin, Bergherr, Elisabeth

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A Balanced Statistical Boosting Approach for GAMLSS via New Step Lengths von Daub, Alexandra, Mayr, Andreas, Zhang, Boyao, Bergherr, Elisabeth

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Topics in the Haystack: Extracting and Evaluating Topics beyond Coherence von Thielmann, Anton, Seifert, Quentin, Reuter, Arik, Bergherr, Elisabeth, Säfken, Benjamin

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Prediction-based Variable Selection for Component-wise Gradient Boosting von Potts, Sophie, Bergherr, Elisabeth, Reinke, Constantin, Griesbach, Colin

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"Spatial Joint Models through Bayesian Structured Piece-wise Additive Joint Modelling for Longitudinal and Time-to-Event Data" von Rappl, Anja, Kneib, Thomas, Lang, Stefan, Bergherr, Elisabeth

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Spatial Confounding in Gradient Boosting von Knieper, Lars, Kneib, Thomas, Bergherr, Elisabeth

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Adaptive Step-Length Selection in Gradient Boosting for Generalized Additive Models for Location, Scale and Shape von Zhang, Boyao, Hepp, Tobias, Greven, Sonja, Bergherr, Elisabeth

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Approximated Gaussian Random Field Under Different Parameterizations for MCMC von Cavieres, Joaquin, Monnahan, Cole C., Bolin, David, Bergherr, Elisabeth

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A Balanced Statistical Boosting Approach for GAMLSS via New Step Lengths von Daub, Alexandra, Mayr, Andreas, Zhang, Boyao, Bergherr, Elisabeth

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Bayesian Boosting for Linear Mixed Models von Zhang, Boyao, Griesbach, Colin, Kim, Cora, Müller-Voggel, Nadia, Bergherr, Elisabeth

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Topics in the Haystack: Extracting and Evaluating Topics beyond Coherence von Thielmann, Anton, Seifert, Quentin, Reuter, Arik, Bergherr, Elisabeth, Säfken, Benjamin

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Prediction-based Variable Selection for Component-wise Gradient Boosting von Potts, Sophie, Bergherr, Elisabeth, Reinke, Constantin, Griesbach, Colin

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Adaptive Step-Length Selection in Gradient Boosting for Generalized Additive Models for Location, Scale and Shape von Zhang, Boyao, Hepp, Tobias, Greven, Sonja, Bergherr, Elisabeth

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