Requirements:
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.
(Assumed) content of the course:

Asymptotic methods  Delta Theorem

Theory of maximum likelihood

Profile, conditional and marginal likelihood

Mestimators and Zestimators, Quasilikelihood, Robust estimation

Bootstrap

Quantile regression

EMalgorithm

Methods for missing data

Kernel density estimation

Kernel nonparametric regression
Exercise classes
Exercise classes accompany the lecture with both theoretical as well as practical examples and illustrations.
Lecture material and actual information
Exercise class 3 (7. 3. 2017)
Exercise class 6 (28. 3. 2017)
Exercise class 7 (4. 4. 2017)
Exercise class 8 (11. 4. 2017)

Working script  nmst434E08.R

Datasets  IQen.RData,
(the code supposes that datasets are placed in the subdirectory `data' of the working directory)
Exercise class 9 (18. 4. 2017)
Exercise class 10 (25. 4. 2017)

Working script  nmst434E10.R

AAUP.RData (the working script assumes
that datasets are stored in the directory 'data' that is placed in the working directory)
Exercise class 11 (2. 5. 2017)
Exercise class 12 (9. 5. 2017)
Exercise class 13 (16. 5. 2017)
Exercise class 14 (23. 5. 2017)