Optimal Designs When the Variance Is A Function of the Mean
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Publication:4666673
DOI10.1111/j.0006-341X.1999.00925.xzbMath1059.62577OpenAlexW1976805930WikidataQ52065213 ScholiaQ52065213MaRDI QIDQ4666673
Publication date: 13 April 2005
Published in: Biometrics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1111/j.0006-341x.1999.00925.x
Applications of statistics to biology and medical sciences; meta analysis (62P10) Optimal statistical designs (62K05)
Related Items (9)
Optimal designs based on the maximum quasi-likelihood estimator ⋮ Optimum treatment allocation for dual-objective clinical trials with binary outcomes ⋮ D-optimal designs for the Mitscherlich non-linear regression function ⋮ Elemental information matrices and optimal experimental design for generalized regression models ⋮ Optimal design of experiments with anticipated pattern of missing observations ⋮ Robust and efficient design of experiments for the Monod model ⋮ Optimal designs for some stochastic processes whose covariance is a function of the mean ⋮ E-optimal design for the Michaelis-Menten model ⋮ Minimax robust designs for regression models with heteroscedastic errors
Cites Work
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- Using residuals robustly I: Tests for heteroscedasticity, nonlinearity
- Bayesian experimental design: A review
- Optimal two-point designs for the michaelis-menten model with heteroscedastic errors
- Least Squares Estimation when the Covariance Matrix and Parameter Vector are Functionally Related
- Estimating Michaelis-Menten Parameters: Bias, Variance and Experimental Design
- D-optimal designs for generalised linear models with variance proportional to the square of the mean
- Correcting Inhomogeneity of Variance with Power Transformation Weighting
- D-Optimum Designs for Heteroscedastic Linear Models
- Optimal and Efficient Designs of Experiments
- Locally Optimal Designs for Estimating Parameters
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