期刊
ANNALS OF STATISTICS
卷 37, 期 6B, 页码 4088-4103出版社
INST MATHEMATICAL STATISTICS
DOI: 10.1214/09-AOS708
关键词
c-optimal design; heteroscedastic regression; Elfving's theorem; pharmacokinetic models; random effects; locally optimal design; geometric characterization
资金
- Deutsche Forschungsgemeinschaft
- NIH [1R01GM072876]
- BMBF
We consider the common nonlinear regression model where the variance, as well as the mean, is a parametric function of the explanatory variables. The c-optimal design problem is investigated in the case when the parameters of both the mean and the variance function are of interest. A geometric characterization of c-optimal designs in this context is presented, which generalizes the classical result of Elfving [Ann. Math. Statist. 23 (1952) 255-262] for c-optimal designs. As in Elfving's famous characterization, c-optimal designs can be described as representations of boundary points of a convex set. However, in the case where there appear parameters of interest in the variance, the structure of the Elfving set is different. Roughly speaking, the Elfving set corresponding to a heteroscedastic regression model is the convex hull of a set of ellipsoids induced by the underlying model and indexed by the design space. The c-optimal designs are characterized as representations of the points where the line in direction of the vector c intersects the boundary of the new Elfving set. The theory is illustrated in several examples including pharmacokinetic models with random effects.
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