Notifications
The course notes have been updated. Section 2.8.1. on Pearson residuals has been extended and improved.
Project assignment has been published. Datasets are available in the SIS, module "Předměty" (Subjects). The access is limited to the students that are registered for the course under either code (login is required). For more details, see below.
Conditions for project completion have been relaxed. You can take the oral part of the exam before the completion of the project. For more details, see below.
Exam terms: Fri May 30, Fri June 6, Mon June 9, Fri June 13, Thu June 19, Thu June 26. Additional exam terms can be provided on individual agreement in July and August. There will be one exam term in the second half of September.
Schedule
| Lectures | |||
| Monday | 15:40 - 17:10 | K1 | |
| Tutorial Classes | |||
| Monday | 9:00 - 10:30 | K3 | Instructor: Šárka Hudecová |
| Monday | 9:00 - 10:30 | K4 | Instructor: Arnošt Komárek |
Course Materials
Supplementary Course Materials
-
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
Three homework assignments will be given during the semester. Each homework solution will be assessed as one of the following: Satisfactory (worth 2 points), Borderline satisfactory (worth 1 point), Unsatisfactory (0 points). Only students who get in total at least 5 points will get the course credit. It is possible to correct one borderline satisfactory report. An unsatisfactory report cannot be corrected. The nature of these requirements precludes any possibility of additional attempts to obtain the tutorial credit. The tutorial credit is necessary to sign up for the exam.
Exam Project
The requirements on the project report are described in the assignment. Feel free to ask questions by email about anything that was not explained in the assignment sufficiently well.
The project assignment varies according to the course code.
Project assignment for PMSE students enrolled in NMST412
Project assignment for FPM students enrolled in NMFP402
Examination
The exam has two parts. To pass the exam, both parts (project + oral part) need to be passed. Students can attend the oral part and evaluation of the project report separately.
-
Oral theoretical part which
differs according to the course code/study program.
- NMFP402 (Financial and Insurance Mathematics): presentation of basic theory for a topic chosen from Chapters 3 or 4 of the course notes (key results from Chapter 2 are also needed).
- NMST412 (PMSE) presentation of theory (incl. derivations and proofs) for a topic chosen from the whole contents of the course.
- Evaluation of the project report. Project evaluations take place at the same exam terms as oral exams. However, you do not register for this part in the SIS. Instead, you email your project report and the associated R code to me at least three working days before the exam date and express your wish to do project evaluation on that date. You will always get back a confirmatory email. Then you come at the scheduled beginning of the exam without having registered in the SIS.