After successful completion of this course, students are able to reproduce basic concepts and techniques used in the field of statistical modeling. They can exemplify these in applied contexts.

Lectures with interactive elements, case studies, exercises.

1)    Statistical modelinga)    Information and learning (e.g. conditional expectation, updating)b)    Linear (regression) models2)    Parameter estimationa)    Estimation principles: Bayesian and frequentist approaches (e.g. maximum likelihood, methods of moments, least squares)b)    Properties of estimators (e.g. unbiasedness, consistency, efficiency, asymptotic normality)3)    Classical hypothesis testinga)    Definitions and basic properties (e.g. Type I/II error, power, size)b)    Likelihood-based tests (e.g. LR, LM, Wald)4)    Model checking 

·     Murphy, K. P. (2012): Machine Learning: A Probabilistic Perspective, MIT Press.·     Fahrmeir, L., Kneib, T., Lang, S., and Marx, B. (2021): Regression: Models, Methods and Applications, 2nd ed., Springer.·     Casella, G. and Berger, R.(2002): Statistical Inference, 2nd ed., Duxbury.

- Individual Exercises- Case Studies- Final Exam (in the last unit, with one possibility of a retake in case of a negative grade in March)

Contents of the course

Number and quality of solved exercisesCorrectness and quality of solved case studiesPerformance at the final exam