The Necessity of Strongly Subadditive Capacities for Neyman-Pearson Minimax Tests.
Topologies associated to some decision statistical functions
Two-point priors and minimax estimation of a bounded parameter under convex loss
The problem of minimax estimation of a parameter θ when θ is restricted to a finite interval [θ₀,θ₀+m] is studied. The case of a convex loss function is considered. Sufficient conditions for existence of a minimax estimator which is a Bayes estimator with respect to a prior concentrated in two points θ₀ and θ₀+m are obtained. An example is presented.