The "Statistical Computing" 2024 conference took place in Reisensburg (Günzburg) at the end of July this year. We were also represented with a small group and our own presentations! In particular, Anna von Plessen and Marisa Lange were able to present the progress of their Master's theses and get a first taste of the conference atmosphere.


Alexandra Daub hpresented an approach on how gradient boosting methods for GAMLSS models can be improved by adaptive step lengths. Her method, which is based on the idea of establishing a balance between the individual base learners, shows promising results in both simulated and real data sets. A preprint was published earlier this year.


Anna von Plessen presented her work, which deals with magnetoencephalography (MEG) data of brain waves. She is developing a concept to improve the processing of this high-dimensional data using methods such as gradient boosting and functional regression. As part of her presentation, she also showed her R Shiny application for visualising MEG data.


Colin Griesbach gave a presentation on model-based gradient boosting methods for GAMLSS models. In this context, he developed a method to improve the estimation of random effects. In his lecture, he presented the results of this method using data from cystic fibrosis patients.


Marisa Lange also works with data from cystic fibrosis patients. In her presentation, she gave a detailed insight into the structure and characteristics of the data and presented modelling approaches using gradient boosting. A particular focus of her work is the processing of spatial information in the data.