Schedule
Lectures | |||
Monday | 15:40 - 17:10 | K1 | |
Tutorial Classes | |||
Friday | 10:40 - 12:10 | K11 | Instructor: Šárka Hudecová |
Friday | 10:40 - 12:10 | K6 | Instructor: Marek Omelka |
Course Materials
Supplementary Course Materials
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Summary of maximum likelihood
estimation theory (pdf)
This is a useful brief summary of the maximum likelihood theory. These results are assumed to be known to the enrolled students and will be used in the course during the whole semester. They are also included in the appendix of the main course notes.
Course Plan
The course covers methods for regression analysis of responses that do not follow the normal distribution, especially of discrete responses.
We will learn to understand some of the common statistical methods for fitting regression models to such data.
The lecture focuses on the development, theoretical justification, and interpretation of these methods.
The tutorial classes will teach how to apply these methods to real problems but may include some theoretical tasks as well. A new assignment will be given about every 2 weeks.
The course will be concluded by a written data analysis project.
Prerequisites
This course assumes mid-level knowledge of linear regression theory and applications. Master students of "Probability, statistics and econometrics" must have completed the course on Linear Regression (NMSA407) prior to enrolling here. Master students of "Financial and Insurance Mathematics" must have completed the course on Financial Econometrics (NMFP401) prior to enrolling here.
Requirements for Tutorial Class Credit
The credit for the tutorial class will be awarded to the student who hands in a satisfactory solution to each assignment by the prescribed deadline.
Final Project
Completion of the project is a requirement for participating in the exam. The project assignment will be published towards the end of the semester.
Examination
The exam has two parts:
- Evaluation of the project report.
- Oral part which differs according to the course code/study program. Requirements will be announced later.
To pass the exam, both parts need to be passed.