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 3 (7. 3. 2017)
Exercise class 6 (28. 3. 2017)
Exercise class 7 (4. 4. 2017)
Exercise class 8 (11. 4. 2017)
Exercise classes accompany the lecture with both theoretical as well as practical examples and illustrations.
Exercise class 9 (18. 4. 2017)
Exercise class 10 (25. 4. 2017)
Working script - nmst434-E08.R
Datasets - IQ-en.RData,
(the code supposes that datasets are placed in the subdirectory `data' of 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)
Working script - nmst434-E10.R
AAUP.RData (the working script assumes
that datasets are stored in the directory 'data' that is placed in the working directory)