Statistical modelling
This page contains information about courses offered by us. Further details are provided in the ekVV.
Generalised linear models (GLMs)
GLMs generalise linear models by allowing for various types of data (e.g., non-normal data) and flexible forms of the linear predictor. In this course, we will discuss various aspects of GLMs, including the formulation of GLMs, model fitting, model selection, and model checking. In addition, we will cover various extensions of GLMs, including generalised linear mixed models (GLMMs), generalised additive models (GAMs), and GAMs for location, scale, and shape (GAMLSS).
Student feedback can be found here.
Hidden Markov models (HMMs)
Hidden Markov models (HMMs) are statistical models for time series where the observed variables are driven by latent states. In this course, we will discuss various aspects of HMMs, including the formulation of HMMs, parameter estimation, model selection, and model checking. In addition, we will cover various extensions of the basic model structure.
Supervision of B.Sc. and M.Sc. theses
If you are interested in writing your B.Sc. or M.Sc. thesis within statistical modelling, please contact us. Prerequisite is the successful completion of the modules 31-M3 Statistics and 31-M9 Data analysis. You can either suggest a topic of your choice or ask us for ideas. Applied theses in collaboration with companies are also possible. However, note that (especially M.Sc.) theses should have a strong methodological component. Useful templates (for both Word and LaTeX) are provided by Prof. Dr. Dietmar Bauer.