Course: Mathematical Statistics

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Course title Mathematical Statistics
Course code KMA/PGSA3
Organizational form of instruction Lecture
Level of course Doctoral
Year of study not specified
Semester Winter and summer
Number of ECTS credits 5
Language of instruction Czech, English
Status of course Compulsory-optional
Form of instruction Face-to-face
Work placements This is not an internship
Recommended optional programme components None
Lecturer(s)
  • Hron Karel, prof. RNDr. Ph.D.
  • Fišerová Eva, doc. RNDr. Ph.D.
Course content
1. Elementary statistical inference 2. Maximum likelihood method 3. Sufficient statistics 4. Hypotheses testing 5. Inference under the normality assumption 6. Nonnaprametric statistics 7. Bayesian statistics 8. Linear models

Learning activities and teaching methods
Work with Text (with Book, Textbook)
  • Preparation for the Exam - 120 hours per semester
Learning outcomes
To get an overview about methods of mathematical statistics.
Comprehension Understanding of basic methods of mathematical statistics.
Prerequisites
Basic course in probability theory (doctoral level).

Assessment methods and criteria
Oral exam

Oral exam: to know and to understand the subject.
Recommended literature
  • Anděl, J. (2011). Základy matematické statistiky. MATFYZPRESS, Praha.
  • Anderson, T. W. (2003). An introduction to multivariate statistical analysis. Hoboken, N.J: Wiley-Interscience.
  • Hogg, R. V., McKean, J. W., Craig, A.T. (2018). Introduction to mathematical statistics. Prentice Hall, Upper Saddle River.
  • Hron, K., Kunderová, P., Vencálek, O. (2018). Základy počtu pravděpodobnosti a metod matematické statistiky. UP Olomouc.
  • James, G., Witten, D., Hastie, T., Tibshirani, R. (2013). An introduction to statistical learning. Springer, New York.


Study plans that include the course
Faculty Study plan (Version) Category of Branch/Specialization Recommended year of study Recommended semester
Faculty: Faculty of Science Study plan (Version): Applied Mathematics (2020) Category: Mathematics courses - Recommended year of study:-, Recommended semester: -