Designs are found for discriminating between two non-Normal models in the presence of prior information. The KL-optimality criterion, where the true model is assumed to be completely known, is extended to a criterion where prior distributions of the parameters and a prior probability of each...

Usually, in the Theory of Optimal Experimental Design the model is assumed to be known at the design stage. In practice, however, more competing models may be plausible for the same data. Thus, a possibility is to find an optimal design which take both model discrimination and parameter...

In this paper some results on the problem of computing optimal designs for discriminating between rival models are provided. Using T-optimality for two rival models a compound criterion is developed to discriminate between more than two models. Surprising results arise when T-optimal designs are...

In the optimal design theory, the T-optimality criterion is useful for the discrimination between two competitive models. This criterion has an interesting statistical interpretation as the power of a test for the fit of a second model when the first one is true. Usually there is not a closed...

In the optimal design theory, the T-optimality criterion is useful for the discrimination between two competitive models. This criterion has an interesting statistical interpretation as the power of a test for the fit of a second model when the first one is true. Usually there is not a closed...