Distances Based on the Perimeter of the Risk Set of a Testing Problem
At the core of this paper is a simple geometric object, namely the risk set of a statistical testing problem on the one hand and f-divergences, which were introduced by Csiszár (1963) on the other hand. f-divergences are measures for the hardness of a testing problem depending on a convex
real valued function f on the interval [0,∞). The choice of this parameter f can be adjusted so as to match the needs for specific applications.
One of these adjustments of the parameter f is exemplified in Section 3 of this paper. There it is illustrated that the appropriate choice of f for the construction of least favourable distributions in robust statistics is the convex function f(u) =√(1 + u^2) −(1+u)/√2 yielding the perimeter of the risk set
of a testing problem.
After presenting the definition, mentioning the basic properties of a risk set and giving the integral geometric representation of f-divergences the paper will focus on the perimeter of the risk set.
All members of the class of f-divergences of perimeter-type introduced and investigated in Österreicher and Vajda (2003) and Vajda (2009) turn out to be metric divergences corresponding to a class of entropies introduced by Arimoto (1971).
Without essential loss of insight we restrict ourselves to discrete probability distributions and note that the extension to the general case relies strongly on the Lebesgue-Radon-Nikodym Theorem.
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