Building on the statistical inference concepts (likelihood and Bayes) from Statistical Inference I, this class will cover more advanced topics relevant in contemporary ('computer-age') statistical inference including
- topics (in particular) relevant in high-dimensional setting: Multiple testing, inference after model selection, reproducibility,
- regularization (Ridge, Lasso) and connections to Bayesian inference
- inference in more complex settings: model misspecification, dependent data, missing data, censored data
- computationally intensive methods: More on Bayesian inference, on the bootstrap and resampling procedures, permutation tests
We will often discuss frequentist and Bayesian approaches to the same problem as well as connections between them.
- Kursverantwortliche/r: Sonja Greven
- Kursverantwortliche/r: Eva-Maria Maier
- Kursverantwortliche/r: Lisa Maike Steyer
- Kursverantwortliche/r: Alexander Volkmann
Semester: SoSe 2022