9 papers · 1 filter
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…
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…