Projekt 4. - Institut for Matematik og Datalogi - Syddansk Universitet

N a t u r v i d e n s k a b e l i g t P r o j e k t / F a r m a c e u t : V a l g f r i t P r o j e k t
Projekt 49. Numerical and statistical aspects of
nonlinear regression
Vejleder: Achim Schroll (achim@imada.sdu.dk) and Yuri Goegebeur
(yuri.goegebeur@stat.sdu.dk )
Institut: IMADA
Praktisk del: The project is theoretical
Gruppeplacering: IMADA
Gruppestørrelse: Min 3 and max 5 participants. Two groups can work on the project.
Kommentarer: Ingen
Keywords:
Nonlinear regression models, least squares, maximum likelihood, model validation
and inference, growth models, Taylor‟s theorem, Gauss-Newton method,
steepest descent method, Quasi-Newton algorithms.
Abstract
A large part of the regression analysis literature deals with linear regression models, that is models
linear in the model parameters. However, in many areas of the physical, chemical, engineering,
and biological sciences, knowledge about the experimental situation suggests the use of a
less empirical, more theoretically based, nonlinear model. It is the objective of the project to
study both the numerical and the statistical issues involved in the fitting of such nonlinear models.
A possible application is for example the global positioning system (GPS).
Software: Maple, Matlab or R: available for IMADA students in the terminalroom.
Minikurser
Obligatorisk: Projektarbejde (LaTeX).
Anbefalede: Ingen
Litteraturliste over metode artikler, som udleveres til de studerende
Sauer, T., 2006. Numerical Analysis, Chapter 4, especially 4.4., Pearson Education.
1. Kutner, M.H., Nachtsheim, C.J., Neter, J., Li, W., 2005. Applied Linear Statistical Models
(Fifth Edition). McGraw-Hill International Edition.
• Myers, R.H., 1990. Classical and Modern Regression with Applications (second edition). PWS
Kent.
Sen, A., Srivastava, M., 1990. Regression Analysis - Theory, Methods, and Applications. Springer-Verlag.
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