Notifications
Project assignment has been published. It is available in the SIS, module "Předměty" (Subjects). The access is limited to the students that are registered for this course.
Exam terms for the oral part will be put in the SIS on request. If there is no convenient exam date for you in the SIS contact me and give a date range when you are going to be ready to take the exam. Project evaluation can be done separately from the oral part (before or after) and will not be scheduled in the SIS. Opportunities to take either part of the exam will be offered throughout the summer till September.
Schedule
| Lectures | |||
| Monday | 9:00 - 10:30 | K3 | |
| Tuesday | 14:00 - 15:30 | K4 | |
| Exercise Class | |||
| Monday | 14:00 - 15:30 | K4 | Instructor: Arnošt Komárek |
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.
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J.C. Pinheiro & D.M. Bates.
Mixed-Effects Models in S and
S-plus.
Springer, New York, 2000.
A good reference on fitting mixed effect models in R (and S-plus).
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P.J. Diggle, K.Y. Liang & S.L. Zeger. Analysis of Longitudinal Data.
Oxford University Press, Oxford, 1994.
Another useful book on GEE, linear mixed models and GLMM.
Course Plan
The course covers methods for regression analysis of data that belong to one or more of the following categories
- do not follow the normal distribution
- violate the assumption of equal variance
- violate the assumption independence
We will learn to understand some of the common statistical methods that allow fitting regression models to such data.
The lecture focuses on the development, theoretical justification, and interpretation of these methods.
The exercise 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) before enrolling here.
Requirements for Credit/Exam
Credit:
The credit for the exercise class will be awarded to the student who hands in a satisfactory solution to each assignment by the prescribed deadline.
Exam:
The exam has two parts:
- Evaluation of project report (has the assignment been completed in all aspects without major errors?)
- Oral part focuses on the ability to propose an acceptable model for a particular practical problem and to demonstrate understanding of the theory underlying the chosen model (incl. derivations and proofs).
To pass the exam, both parts need to be passed.