D-Optimum Designs for Heteroscedastic Linear Models
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Publication:4836981
DOI10.2307/2291144zbMath0818.62063OpenAlexW4253434975MaRDI QIDQ4836981
R. Dennis Cook, Anthony C. Atkinson
Publication date: 21 June 1995
Published in: Journal of the American Statistical Association (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.2307/2291144
meandesign constructiongeneral equivalence theoremBayesian designTaguchi methodsnonlinear variance functionoff-line quality controlfunction of explanatory variablesoptimality checkssecond-order two-factor response surface
Related Items (16)
Construction of experimental designs for estimating variance components ⋮ Optimal weighted bayesian design applied to dose-response-curve analysis ⋮ Robustness properties of minimally-supported Bayesian D-optimal designs for heteroscedastic models ⋮ Elemental information matrices and optimal experimental design for generalized regression models ⋮ Robust and efficient design of experiments for the Monod model ⋮ E-optimal design for the Michaelis-Menten model ⋮ On Bayesian \(D\)-optimal design criteria and the general equivalence theorem in joint generalized linear models for the mean and dispersion ⋮ Examples of the use of an equivalence theorem in constructing optimum experimental designs for random-effects nonlinear regression models ⋮ Heteroscedastic additive models - Estimating the fixed effects and covariance matrix parameters ⋮ Optimal designs for variance function estimation using sample variances ⋮ Optimal Designs When the Variance Is A Function of the Mean ⋮ A general approach to \(D\)-optimal designs for weighted univariate polynomial regression models ⋮ Optimal designs for regression models with a constant coefficient of variation ⋮ A geometric characterization of \(c\)-optimal designs for heteroscedastic regression ⋮ Minimax robust designs for wavelet estimation of nonparametric regression models with autocorrelated errors ⋮ Efficient Bayesian designs under heteroscedasticity
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