Applications of Likelihood Asymptotics for Nonlinear Regression in Herbicide Bioassays

DOI10.1111/j.0006-341X.2000.01204.xzbMath1060.62580WikidataQ73326320 ScholiaQ73326320MaRDI QIDQ4670483

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Publication date: 22 April 2005

Published in: Biometrics (Search for Journal in Brave)

zbMATH Keywords

predictiondetection limitnonlinear regressionvariance heterogeneitylogistic functionlikelihood asymptoticsmodified directed likelihoodherbicide bioassaylikelihood profile plot

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) General nonlinear regression (62J02)

Related Items (4)

Accounting for uncertainty in heteroscedasticity in nonlinear regression ⋮ Accurate parametric inference for small samples ⋮ Asymptotics and the theory of inference ⋮ Statistical monitoring of heteroscedastic dose-response profiles from high-throughput screening

Cites Work

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• Prediction and asymptotics
• An explicit large-deviation approximation to one-parameter tests.
• Use and Abuse of Variance Models in Regression
• Random-Effect Models in Nonlinear Regression with Applications to Bioassay
• Modified signed log likelihood ratio
• Variance functions and the minimum detectable concentration in assays
• Miscellanea. A note on modified estimative prediction limits and distributions
• The Effect of Variance Function Estimation on Nonlinear Calibration Inference in Immunoassay Data

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