This subject contains a 2 day Optional Residential School.Lecture/workshop
Most statistical procedures implicitly assume a linear model. This subject investigates the modelling of truly nonlinear data via applications. While investigation of the theory of nonlinear estimation is necessary, an empirical approach to demonstration of the technique is given emphasis by the use of computer packages using real data. Advanced topics such as the use of curvature measures are introduced.
No offerings have been identified for this subject in 2021.
HD/FL
One session
School of Science and Technology
Introduction The main features of the nonlinear regression model are highlighted with comparison to the generalized linear model and transformations. The use of a statistical software package (S-PLUS) for nonlinear regression models is also covered. Theory of Estimation This topic focuses on the mathematics underlying the nonlinear regression model. Normal equations, the linearization technique and the geometry of least squares for the nonlinear case are covered. Problems with search procedures are also highlighted. Fitting Procedures This topic introduces the different types of fitting procedures for nonlinear regression models including linearization, Newton Raphson, Steepest Descent and Marquardt's Correction. The problems associated with the choice of starting values are discussed. The use of curvature measures and the connection to model parameterization are a key aspect of the topic. Inference The use and practical application of curvature measures to assess nonlinearity are expounded with particular emphasis on statistical software packages such as S-PLUS. Model assessment and the assumptions underpinning the nonlinear regression model are discussed.
This subject contains a 2 day Optional Residential School.Lecture/workshop
The information contained in the CSU Handbook was accurate at the date of publication: May 2021. The University reserves the right to vary the information at any time without notice.