SS26: Topics in Inferential Statistics (with R)

Seminar Description

This seminar examines approaches to statistical inference that extend beyond the classical linear model. Emphasis is placed on understanding how inferential conclusions are affected by model complexity, data imperfections, and modern estimation techniques, alongside practical implementation in R. Students are expected to be comfortable with linear and generalized linear models, including mixed-effects models. Possible topics include inference with missing data via multiple imputation, generalized additive models, mixture models and latent structure, multiple testing and false discovery rate control, penalized regression methods (e.g. ridge and lasso), and tree-based methods such as random forests. Throughout the seminar, methods are evaluated not only for their predictive performance but also for the validity, interpretation, and robustness of the inferences they support. Students will engage with the statistical literature, present a topic and prepare a hands-on session for their fellow students.

Requirements: Only students who successfully participated in Statistics with R may join.