Three PhD students of our Chair presented their research progress at the CompStat conference in Giessen. CompStat offered a platform to present their research to an international audience and to receive valuable input. Quentin Seifert presented in the session on "Functional data analysis", Lars Knieper and Sophie Potts contributed in the session "Applied statistics and data analysis". The abstracts of the presentations can be viewed here.

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 presented 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.

This year the International Workshop on Statistical Modelling (IWSM) took place in Durham, UK. We actively participated in the annual conference again this year and are proud to have received an award!

Dr. Colin Griesbach opened the conference with his presentation on model-based gradient boosting methods for GAMLSS models applied to data from cystic fibrosis patients. The conference article has been published in the conference proceedings.

Lars Knieper gave a presentation on the phenomenon of spatial confounding, which can be resolved in model-based gradient boosting using the spatial+ approach. He illustrated his approach using AirBnB data. The corresponding short article has been published by Springer.

Quentin Seiferts paper entitled ‘Function-on-scalar regression via first-order gradient-based optimization’ combines classical statistics with elements from neural networks and allows to analyse a large amount of parking data during the COVID-19 pandemic and identify patterns in shopping behaviour.

Dr. Joaquin Cavieres also presented part of his research comparing approximated Gaussian random fields with different parameterization approaches. The article has also been published.

Sophie Potts presented her work on the application of joint models for longitudinal and time-to-event data in the social sciences as a poster. The method, which originated in biostatistics, was applied to an example from family sociology. Sophie's novel application and accessible presentation was honoured with the ‘Best Student Poster Award’. The award-winning poster can now be admired in the corridor of our offices.

After almost two years of work funded by the MWK, we have a broad collection of Shiny Apps to help explain various statistical concepts. They are available here. Most of them were created directly through specific lectures, but also work as stand-alone version for self-studying. Have fun exploring them!