5 citations · 5 across the 10 of their papers we have counts for
10 papers
A unified framework for spatially resolved cortical activation analysis
Lars Knieper, Nadia Müller-Voggel, Tobias Hepp +2
Cluster-based permutation tests are widely used for analyzing MEG data, even though they are limited to cluster-level inference and do not provide spatially resolved effect estimat…
Adressing Separation: A Firth-corrected Joint Model for Longitudinal and Time-to-event Data with an Application on Dropout from Vocational Training
Sophie Potts, Viola Deutscher, Elisabeth Bergherr
Joint Models for longitudinal and time-to-event data are frequently used to model endogenous longitudinal covariates alongside a time-to-event outcome. However, the model class bor…
Estimating Zero-inflated Negative Binomial GAMLSS via a Balanced Gradient Boosting Approach with an Application to Antenatal Care Data from Nigeria
Alexandra Daub, Elisabeth Bergherr
Statistical boosting algorithms are renowned for their intrinsic variable selection and enhanced predictive performance compared to classical statistical methods, making them espec…
A Balanced Statistical Boosting Approach for GAMLSS via New Step Lengths
Alexandra Daub, Andreas Mayr, Boyao Zhang +1
Component-wise gradient boosting algorithms are popular for their intrinsic variable selection and implicit regularization, which can be especially beneficial for very flexible mod…
Topics in the Haystack: Extracting and Evaluating Topics beyond Coherence
Anton Thielmann, Quentin Seifert, Arik Reuter +2
Extracting and identifying latent topics in large text corpora has gained increasing importance in Natural Language Processing (NLP). Most models, whether probabilistic models simi…
Prediction-based Variable Selection for Component-wise Gradient Boosting
Sophie Potts, Elisabeth Bergherr, Constantin Reinke +1
Model-based component-wise gradient boosting is a popular tool for data-driven variable selection. In order to improve its prediction and selection qualities even further, several…
"Spatial Joint Models through Bayesian Structured Piece-wise Additive Joint Modelling for Longitudinal and Time-to-Event Data"
Anja Rappl, Thomas Kneib, Stefan Lang +1
Joint models for longitudinal and time-to-event data have seen many developments in recent years. Though spatial joint models are still rare and the traditional proportional hazard…
Bayesian Boosting for Linear Mixed Models
Boyao Zhang, Colin Griesbach, Cora Kim +2
Boosting methods are widely used in statistical learning to deal with high-dimensional data due to their variable selection feature. However, those methods lack straightforward way…
Adaptive Step-Length Selection in Gradient Boosting for Generalized Additive Models for Location, Scale and Shape
Boyao Zhang, Tobias Hepp, Sonja Greven +1
Tuning of model-based boosting algorithms relies mainly on the number of iterations, while the step-length is fixed at a predefined value. For complex models with several predictor…
Addressing cluster-constant covariates in mixed effects models via likelihood-based boosting techniques
Colin Griesbach, Andreas Groll, Elisabeth Waldmann
Boosting techniques from the field of statistical learning have grown to be a popular tool for estimating and selecting predictor effects in various regression models and can rough…