Notifications

Project assignment has been revealed.

Exam terms for the oral part will be published in the SIS on request. If there is no suitable date in the SIS contact me and give a 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 most of the summer till the end of September.

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.

Schedule 

Lectures
Tuesday   9:00 - 10:30 K6  
Wednesday 12:20 - 13:50 K2  
Exercise Class
Thursday 12:20 - 13:50 K11 Instructor: Arnošt Komárek

Course Materials

Unfortunately, no single existing book covers the subject sufficiently widely and deeply. Special class notes are available and will be published here as the course develops.

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

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:

  1. Evaluation of project report (has the assignment been completed in all aspects without major errors?)
  2. 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.