Abstract:In discrete choice experiments, the information matrix depends on the model parameters. Therefore, D-optimal designs are only locally optimal in the parameter space. This dependence renders the optimization problem very difficult, as standard theory encodes D-optimality in systems of highly nonlinear equations and inequalities. In this work, we connect design theory for discrete choice experiments with Laplacian matrices of connected graphs. We rewrite the D-optimality criterion in terms of Laplacians via Kirc… Show more
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