On the regularization of singular c-optimal designs
Keyword(s):
AbstractWe consider the design of c-optimal experiments for the estimation of a scalar function h(θ) of the parameters θ in a nonlinear regression model. A c-optimal design ξ* may be singular, and we derive conditions ensuring the asymptotic normality of the Least-Squares estimator of h(θ) for a singular design over a finite space. As illustrated by an example, the singular designs for which asymptotic normality holds typically depend on the unknown true value of θ, which makes singular c-optimal designs of no practical use in nonlinear situations. Some simple alternatives are then suggested for constructing nonsingular designs that approach a c-optimal design under some conditions.
2018 ◽
Vol 7
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pp. 543
1993 ◽
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pp. 13-17
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1984 ◽
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pp. 139-142
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2008 ◽
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pp. 1716-1739
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pp. 183-192
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pp. 15-18
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2016 ◽
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1988 ◽
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pp. 385-391
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