Further Consideration of Heteroscedasticity in Fitting Kinetic Models

Abstract

Box and Hill [6] recently proposed a method for using power transformation weighting in least squares analysis to account for changing variance. Such an approach can be useful when the original data are heteroscedastic but adequate weight estimates are not available, and when the original data are homoscedastic but heteroscedasticity is induced by the data analyst in linearising a nonlinear model.

Several aspects of their proposal are examined for practical implications in fitting chemical kinetic models and a more direct algorithm is recommended for fitting nonlinear models to heteroscedastic data. Methods for testing model adequacy and assessing parameter precision in such situations are also discussed.

KEY WORDS:

• Heteroscedasticity
• Weighted least squares
• Power transformations
• Chemical kinetics
• Bayesian approach