** Exercise class: ** Tuesday 17:20 - 18:50 K3 (S. Nagy)

** Assumed knowledge: **

The knowledge of the statistics and probability theory at the level of courses Mathematical Statistics 1 and 2 (NMSA331 and NMSA332), Probability Theory 1 (NMSA333) and Linear regression (NMSA407).

Among others we will use the following concepts: *almost sure convergence, convergence in probability, convergence in
distribution, law of large numbers and central limit theorem for independent and identically distributed random vectors*.
A nice overview (in Czech language) of most of the results that are used in the course is available here.

- 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

- Current version of the course notes.
- EM algorithm - the script contains the implementation of the EM algorithm for interval censoring in exponential distribution and for fitting a mixture of two normal densities.
- Some information about the exam.