Generalized linear models, spring 2010

Lecturer

dos. Juha Karvanen

Scope

6 cu + optional practical work (harjoitustyö) of 2 cu.

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Type

Advanced studies.

Prerequisites

Generalized linear models are an extension of the usual linear model, where the response variable may be discrete or have a skew distribution. The course covers the generic likelihood-based estimation and test theory of generalized linear models. The most important special cases, like logistic and log-linear models, are treated in more detail.

The course will be a balanced mixture of statistical theory and practical data analysis. R (http://www.r-project.org) is used for data analysis and simulations. Examples from application areas, such as epidemiology and physics, will be presented.

Prerequisites

• Basics concepts of
  • mathematical analysis (derivatives and integrals)
  • matrix algebra (up to multiplication and inversion of matrices)
  • probability (random variables, standard distributions, expectation, variance, independence)
  • statistical inference (maximum likelihood, hypothesis testing).
• Knowledge of linear models is also useful.

Course material

Lectures

Period IV

Wednesdays, starting from 24. March, 14-18 in: 

24. March, 14., 21., 28. April, 5., 12. May  in Unioninkatu 40, U40 Class 6

31. March in University main building, Class 15

Easter holiday 1.-7.4.

Exams

24 May 2010, 16-18 C323
10 June 2010, 10-12 Summer exam (register by 31 May)

Bibliography

• McCullagh, P. & Nelder, J.A. Generalized linear models, 2nd edn, Chapman & Hall, 1989.
• Course material

Registration

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Exercises