Articlediamond1970

The Sequential Generation of $D$-Optimum Experimental Designs

465 citations0/0 files storedThe Annals of Mathematical Statistics

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Optimal Experimental Design Methods Advanced Statistical Methods and Models Statistical and numerical algorithms

Abstract

It is possible to obtain convergence to a $D$-optimum measure, as defined by Kiefer and Wolfowitz, by successively adding points to a given initial experimental design. The points added correspond to points of maximum variance of the usual least squares estimate of the response mean for the particular regression model at each stage. A new bound is given for the generalized variances involved and an example is worked out.

Citations in this library

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References

1959Optimum Designs in Regression Problems

Cited by

1989Optimal Bayesian design applied to logistic regression experiments 2000An Algorithm for the Construction of "D-Optimal" Experimental Designs 1973Some General Points in the Theory of Optimal Experimental Design 1975A Biometrics Invited Paper. A Review of Response Surface Methodology from a Biometric Viewpoint 2009Response Surface Methodology 1997Optimal design in random-effects regression models 1975D-Optimality for Regression Designs: A Review 1972Results in the Theory and Construction of D -Optimum Experimental Designs

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