Generalised Linear Models

7.5 credits

Syllabus, Master's level, 1MS019

This course has been discontinued.

A revised version of the syllabus is available.

Code: 1MS019
Education cycle: Second cycle
Main field(s) of study and in-depth level: Mathematics A1N
Grading system: Fail (U), Pass (3), Pass with credit (4), Pass with distinction (5)
Finalised by: The Faculty Board of Science and Technology, 15 March 2007
Responsible department: Department of Mathematics

Entry requirements

BSc, Analysis of Regression and Variance

Learning outcomes

In order to pass the course (grade 3) the student should be able to

give an account of important concepts and definitions in the area of the course;

exemplify and interpret important concepts in specific cases;

use the theory, methods and techniques of the course to solve mathematical statistical problems;

express problems from relevant areas of applications in a form suitable for further mathematical statistical analysis, choose suitable models and solution techniques;

interpret and asses results obtained;

use statistical software;

present mathematical statistical arguments to others.

Higher grades, 4 or 5, require a higher level of proficiency. The student should be able to treat and solve problems of greater complexity, i.e. problems requiring a combination of ideas and methods for their solution, and be able to give a more detailed account of the proofs of important theorems and by examples and counter-examples be able to motivate the scope of various results.

Requirements concerning the student's ability to present arguments and reasoning are greater.

Content

Linear statistical models, generalised linear models. Likelihood-based inference. Models for discrete data. Logistic regression. Analysis of contingency tables. Introduction to log-linear models. Estimation and model fitting. Residual analysis. Quasi-likelihood methods. Practical examples from different application areas with emphasis on medical applications.

Instruction

Lectures, problem solving sessions and computer-assisted laboratory work.

Assessment

Written examination at the end of the course. Compulsory assignments and laboratory work during the course.