Statistical Inference II (SS 22)

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.