Bayesian optimum designs for discriminating between models with any distribution
Computational Statistics & Data Analysis
Optimal experimental designs for partial likelihood information
Computational Statistics & Data Analysis
Computing efficient exact designs of experiments using integer quadratic programming
Computational Statistics & Data Analysis
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The problem of constructing optimal designs when some of the factors are not under the control of the experimenters is considered. Their values can be known or unknown before the experiment is carried out. Several criteria are taken into consideration to find optimal conditional designs given some prior information on the factors. In order to determine these optimal conditional designs a class of multiplicative algorithms is provided. Optimal designs are computed for illustrative, but simplistic, examples. Two real life problems in production models and a physical test for predicting morbidity in lung cancer surgery motivate the procedures provided.