Abstract
AbstractWeighted least squares estimation is used for fitting nonlinear models to Ames test data. The models are biologically plausible, incorporating mutagenicity, toxicity, and/or saturation. Regressions are weighted to compensate for variance that changes with mean and to attain outlier resistance.
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