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Applications of Likelihood Asymptotics for Nonlinear Regression in Herbicide Bioassays
Author(s) -
Bellio R.,
Jensen J. E.,
Seiden P.
Publication year - 2000
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.0006-341x.2000.01204.x
Subject(s) - nonlinear regression , bioassay , statistics , inference , statistical hypothesis testing , regression , nonlinear system , statistical inference , mathematics , regression analysis , econometrics , computer science , artificial intelligence , biology , ecology , physics , quantum mechanics
Summary. Dose–response models are intensively used in herbicide bioassays. Despite recent advancements the development of new herbicides, statistical analyses are commonly based on asymptotic approximations 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 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.