ALEXANDRA DAUB

I am a PhD student doing methodological research in the field of stastical boosting, with a particular focus on Generalized Additive Models for Location, Scale and Shape (GAMLSS). Statistical boosting combines techniques from machine learning and statistical modelling, enabling the estimation of interpretable models with strong predictive performance as well as an intrinsic variable selection.


As part of the Data Science Hub, I contributed to the creation of our webapp collection.

RESEARCH INTERESTS

• Statistical Boosting


• Generalized Additive Models for Location, Scale and Shape

TEACHING

• Multivariate Statistics (SoSe 2022, SoSe 2023, SoSe 2026)


• Current Topics in Applied Statistics (WS 2025/26)


• Stochastic Processes (SoSe 2024, SoSe 2025)


• Spatial Statistics (WS 2022/23, WS 2023/24)

PUBLICATIONS

• Daub, A., Bergherr, E. (2026) Estimating zero-inflated negative binomial GAMLSS via a balanced gradient boosting approach with an application to antenatal care data from Nigeria. ArXiv


• Daub, A., Mayr, A., Zhang, B., Bergherr, E. A balanced statistical boosting approach for GAMLSS via new step lengths. Comput Stat 40, 4741–4773 (2025). DOI

CONFERENCE CONTRIBUTIONS

• IWSM 2025: Estimating zero-inflated negative binomial GAMLSS via gradient boosting with an application to antenatal care data in Nigeria (Talk)


• DagStat 2025: Balanced boosting for GAMLSS using adaptive step lengths – with an application to antenatal care data in West African countries (Talk)


• Statistical Computing 2024 A Balanced Statistical Boosting Approach for GAMLSS via New Step Lengths (Talk)


• Statistical Computing 2024 Gradient boosting for GAMLSS using adaptive step lengths (Talk)


• IWSM 2023: Gradient boosting for GAMLSS using adaptive step lengths (Poster)


• DAGStat 2022: Modeling the antenatal care of women in West Africa by a GAMLSS using a gradient boosting algorithm with an adaptive step-length (Talk)