title: Applications of likelihood asymptotics for nonlinear regression in herbicide bioassays.
abstract: Dose-response models are intensively used in herbicide bioassays. Despite recent advancements in the development of new herbicides, statistical analyses 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 presented provide accurate approximation for the distribution of test statistics and for prediction limits. Analyses of the fit and measures of detection limits of the bioassays are considered, and the potential of the methods is illustrated by examples with real data.
authors: Department of Statistics, University of Udine, Via Treppo 18, 33100 Udine, Italy. ruggero.bellio@dss.uniud.it
authors: R
authors: R
authors: Bellio
authors: J E
authors: JE
authors: Jensen
authors: P
authors: P
authors: Seiden
mesh_terms: D001681:Biological Assay / Q000379:methods*; D001699:Biometry / Q000379:methods*; D016001:Confidence Intervals; D006540:Herbicides / Q000633:toxicity*; D016013:Likelihood Functions; D010944:Plants / Q000187:drug effects; D012044:Regression Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013449:Sulfonamides; D014227:Triazines / Q000633:toxicity
chemical_list: D006540:Herbicides; D013449:Sulfonamides; D014227:Triazines; C037137:chlorsulfuron