Francisco Javier Alonso Morales , José Miguel Angulo Ibáñez , María del Carmen Bueso Sánchez , María Dolores Ruiz Medina
applications, and plays an important role in risk assessment in relation to detection and prediction of extreme values or threshold exceedances. Given the intermittent character of extremal events, a model-based dynamic selection incorporating the historical sample information constitutes a natural way of constructing a time-adaptive monitoring network.
In this context, we formulate a multi-objective optimization criterion, de ned in terms of a convex linear combination involving an entropy measure of the information contained in the data and a penalty function related to the network structure. The criterion proposed is applied to some numerical examples where di erent models for the spatio-temporal dependence as well as for the local variability are assumed.
This work has been supported by grants P05-FQM-00990, Andalusian CICYE, and MTM2005-08597, DGI, Spain
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