期刊
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
卷 108, 期 504, 页码 1411-1420出版社
AMER STATISTICAL ASSOC
DOI: 10.1080/01621459.2013.806268
关键词
Convergence; Locally optimal design; Multistage design; Phi(p)-optimality
资金
- NSF [DMS-0707013, DMS-1322797]
- FDA MCM Challenge Grant
- Donald W. Reynolds Journalism Institute
- Direct For Mathematical & Physical Scien
- Division Of Mathematical Sciences [1322797] Funding Source: National Science Foundation
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.
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