Document Type
Article
Abstract
Finding 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 still mainly depends on numerical approaches. There is need for a general and efficient algorithm that 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 multistage design strategies. We prove convergence to the optimal design, and show in various examples that the new algorithm outperforms the current state-of-the-art algorithms.
Publication Date
1-1-2013
Recommended Citation
Yang, Min, Stefanie Biedermann and Yihui Elina Tang (2013), “On Optimal Designs for Nonlinear Models: A General and Efficient Algorithm”, Journal of the American Statistical Association, 108 (504), 1411 – 1420.
Original Citation
Yang, Min, Stefanie Biedermann and Yihui Elina Tang (2013), “On Optimal Designs for Nonlinear Models: A General and Efficient Algorithm”, Journal of the American Statistical Association, 108 (504), 1411 – 1420.
Legacy Department
Department of Marketing
Language
eng
Publisher
Taylor & Francis
Rights Statement
In Copyright
