## Notifications

Project assignment has been revealed.

## Schedule

Lectures | |||

Wednesday | 9:00 - 10:30 | K2 | |

Friday | 12:20 - 13:50 | K4 | |

Exercise Class | |||

Thursday | 15:40 - 17:10 | K11 | Instructor: Arnošt Komárek |

## Final Project

The assignment explains all that is needed. If you feel you need to know more, ask questions. The report from the final project is due two working days before the date of project evaluation (see below - requirements for exam).

## Course Materials

Course notes containing definitions, theorems, and explanations (but not proofs or examples) are available on this web page. There will be minor updates in the course notes published here during the semester.

Here 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.

A good reference on fitting mixed effect models in R (and S-plus) is

J.C. Pinheiro & D.M. Bates. Mixed-Effects Models in S and S-plus. Springer, New York, 2000.

Another useful book on GEE, linear mixed models and GLMM is

P.J. Diggle, K.Y. Liang & S.L. Zeger. Analysis of Longitudinal Data. Oxford University Press, Oxford, 1994.

## 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 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 understanding the theory (incl. derivations and proofs). Three questions on three different topics will be asked.

To pass the exam, both parts need to be passed. The parts can be taken on separate dates, in any order. Both parts require physical presence of the student (i.e., even the project report will be discussed with the student, not just by mail). The exam terms in the SIS are for the oral theoretical exam; terms for project evaluation can be set up individually by email.