The knowledge of the statistics and probability theory at the level of
courses Mathematical Statistics 1 and 2
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
M-estimators and Z-estimators, Quasi-likelihood, Robust estimation
Methods for missing data
Kernel density estimation
Kernel nonparametric regression
Lecture material and actual information
Exercise class 11 (2. 5. 2017)
Exercise class 12 (9. 5. 2017)
Exercise class 13 (16. 5. 2017)
Exercise class 14 (23. 5. 2017)
Exercise classes accompany the lecture with both theoretical as well as practical examples and illustrations.