3 papers
stat.ME2026
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…
stat.ME2026
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…
stat.ME2024
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…