** Exercise class: ** Monday 14:00 - 15:30 K2 (S. Nagy)

** Assumed knowledge: **

It is assumed that the students have already a very solid knowledge of statistics and probability theory. This is covered by the following courses Mathematical Statistics 1 and 2
(NMSA331
and NMSA332),
Probability Theory 1 (NMSA333)
and Linear regression (NMSA407).

The student who have not passed these courses (e.g. students coming within the Erasmus+
programme) should check if they are familiar with the theory covered in:

*Mukhopadhyay, N. (2000). Probability and statistical inference. CRC Press* (the whole book except for Chapters 10 and 13);

*Khuri, A. I. (2009). Linear model methodology. Chapman and Hall/CRC* (Chapters 1 - 6).

- Asymptotic methods - Delta Theorem, Moment estimators
- Theory of maximum likelihood
- Profile, conditional and marginal likelihood
- M-estimators and Z-estimators, Robust estimation
- Bootstrap
- Quantile regression
- EM-algorithm
- Methods for missing data
- Kernel density estimation
- Kernel nonparametric regression

- Some information about the exam.
- Course notes for the academic year 2018/2019 course notes.