Optimal two-point designs for the michaelis-menten model with heteroscedastic errors

DOI10.1080/03610929808832173zbMath0901.62095OpenAlexW2115405219MaRDI QIDQ3842906

Weng Kee Wong, Dale Song

Publication date: 20 August 1998

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610929808832173

zbMATH Keywords

heteroscedasticitycontinuous designBayesian \(D\)-optimal designsreceptor assays

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Optimal statistical designs (62K05)

Related Items

Optimal designs based on the maximum quasi-likelihood estimator, Optimal designs for non-competitive enzyme inhibition kinetic models, Optimal design of experiments with anticipated pattern of missing observations, On the Choice of a Prior for Bayesian D-Optimal Designs for the Logistic Regression Model with a Single Predictor, Optimal Designs When the Variance Is A Function of the Mean, Optimal designs for rational regression models

Cites Work

• A general Bahadur representation of \(M\)-estimators and its application to linear regression with nonstochastic designs
• A note on Bayesian \(c\)-and \(D\)-optimal designs in nonlinear regression models
• D-Optimal Designs for Partially Nonlinear Regression Models
• Analysing Data from Hormone-Receptor Assays
• Estimating Michaelis-Menten Parameters: Bias, Variance and Experimental Design
• On the Equivalence of Constrained and Compound Optimal Designs
• A graphical approach for the construction of constrained a and l-optimal designs using efficiency plots
• Locally Optimal Designs for Estimating Parameters