Transformation on both sides of a nonlinear regression model has been used in practice to achieve, for example, linearity in the parameters of the model, approximately normally distributed errors, and constant error variance. The method of maximum likelihood is the most common method for estimating the parameters of the nonlinear model and the transformation parameter. In this talk we will discuss a new method, which we call the Anova method, for estimating all the parameters of the transform-both-sides nonlinear model. The Anova method is computationally simpler than the maximum likelihood approach and and allows a more natural separation of different sources of lack-of-fit.

Considering the Michaelis-Menten model as an example, we will show the results of a simulation study for comparing maximum likelihood and Anova methods, where the Box-Cox transformation is used for transforming both sides of the Michaelis-Menten model. We will also show the use of the Anova method in fitting more complex transform-both-sides nonlinear models, such as transform-both-sides nonlinear mixed effects models and transform-both-sides nonlinear model with random block effects. At the end of the talk, we will briefly present a new approach of designing transform-both-sides nonlinear Michaelis-Menten model.

# Design and analysis of transform-both-sides nonlinear models

Speaker:

Mahbub Latif

School of Mathematical SciencesQueen Mary

Date/Time:

Thu, 28/01/2010 - 16:30

Room:

M203

Seminar series: