Applications of likelihood asymptotics for nonlinear regression in herbicide bioassays

Dose-response models are intensively used in herbicide bioassays. Despite r ecent advancements in the development of new herbicides, statistical analys es are commonly based on asymptotic approximations that are sometimes poor. This paper presents the use of recent results in higher order asymptotics for Likelihood-based inference in nonlinear regression. The methods present ed provide accurate approximation for the distribution of test statistics a nd for prediction limits. Analyses of the fit and measures of detection lim its of the bioassays are considered, and the potential of the methods is il lustrated by examples with real data.