D-optimum designs in multi-factor models with heteroscedastic errors
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Publication:707073
DOI10.1016/j.jspi.2003.12.013zbMath1089.62090OpenAlexW1982497340WikidataQ57529031 ScholiaQ57529031MaRDI QIDQ707073
Publication date: 9 February 2005
Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.jspi.2003.12.013
Additive modelsD-optimum designsEfficiency functionInformation matricesKronecker product modelsProduct designs
Related Items (12)
Optimal designs for multi-factor nonlinear models based on the second-order least squares estimator ⋮ Locally and maximin optimal designs for multi-factor nonlinear models ⋮ Bayesian optimal designs for multi-factor nonlinear models ⋮ A note on \(R\)-optimal designs for multi-factor models ⋮ R-optimal designs for multi-factor models with heteroscedastic errors ⋮ Minimax A‐, c‐, and I‐optimal regression designs for models with heteroscedastic errors ⋮ Optimal experimental designs for treatment contrasts in heteroscedastic models with covariates ⋮ Locally \(D\)-optimal designs for heteroscedastic polynomial measurement error models ⋮ Bayesian and maximin optimal designs for heteroscedastic multi-factor regression models ⋮ Design optimality in multi-factor generalized linear models in the presence of an unrestricted quantitative factor ⋮ A-optimal designs for heteroscedastic multifactor regression models ⋮ Optimal designs for multiple treatments with unequal variances
Cites Work
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- A generalization of D- and \(D_ 1\)-optimal designs in polynomial regression
- \(G\)-optimal designs for multi-factor experiments with heteroscedastic errors
- Optimum designs for multi-factor models
- On \(G\)-efficiency calculation for polynomial models
- A new design criterion when heteroscedasticity is ignored
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