Yang, Min, Biedermann, Stefanie and Tang, Elina
On optimal designs for nonlinear models: a general and
Journal of the American Statistical Association, 108, (504), . (doi:10.1080/01621459.2013.806268).
Deriving optimal designs for nonlinear models is challenging in general. Although some recent results allow us to focus on a simple subclass of designs for most problems, deriving a specific optimal design mainly depends on algorithmic approaches. There is need of a general and efficient algorithm which is more broadly applicable than the current state of the art methods. We present a new algorithm that can be used to find optimal designs with respect to a broad class of optimality criteria, when the model parameters or functions thereof are of interest, and for both locally optimal and multi- stage design strategies. We prove convergence to the desired optimal design, and show that the new algorithm outperforms the best available algorithm in various examples
Actions (login required)