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1986•The Japanese Journal of PharmacologyOpen access

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

Keita Kamikubo, Hiroshi Murase, Masanori A. Murayama, Kiyoshi Miura

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Abstract

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.

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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.

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Available abstract

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.

Key concepts: Akaike information criterion, Bayesian information criterion, Nonlinear regression, Statistics, Mathematics, Linear regression, Microcomputer, Regression analysis

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