Data-based decisions under imprecise probability and least favorable models
Data-based decision theory under imprecise probability has to deal with optimization problems where direct solutions are often computationally intractable. Using the Gamma-minimax optimality criterion. the computational effort may significantly be reduced in the presence of a least favorable model. Buja [A. Buja, Simultaneously least favorable experiments. I. Upper standard functionals and sufficiency, Zeitschrift fur Wahrscheinlichkeits-heorie und Verwandte Gebiete 65 (1984) 367-384] derived a necessary and sufficient condition for the existence of a least favorable model in a special case. The present article proves that essentially the same result is valid in case of general coherent upper previsions. This is done mainly by topological arguments in combination with some of Le Cam's decision theoretic concepts. It is shown how least favorable models could be used to deal with situations where the distribution of the data as well as the prior is allowed to be imprecise. (C) 2008 Elsevier Inc. All rights reserved.
imprecise probability least favorable model decision theory coherent upper prevision le cam robust statistics huber-strassen theory equivalence of models