Microcomputer-Based Nonlinear Regression Analysis of Ligand-Binding Data: Application of Akaike's Information Criterion

Akaike's information criterion (AIC) (Akaike, H., IEEE Trans. Automat. Contr. AC-19, 716-723 (1974)) was applied to estimate statistically the number of classes of binding sites from ligand-binding data. Several sets of data were analyzed by both the AIC method and the F-test method. Good agreement was obtained between results from both methods. The present results suggest that the AIC method can be a good alternative to the F-test to estimate the number of classes of sites.