Estimators from Convex Geometries of the Simplex

Abstract: We unify several geometries of the probability simplex under one principle, their generation by a potential: convex potentials produce the Bregman divergences and dually flat structures of information geometry, and the exponentially concave potentials of Pal and Wong produce the logarithmic divergences and their projectively flat structures. Each structure carries a duality between maximum entropy and minimum divergence and, with it, a projection principle for estimation: the U-estimators of Eguchi, Komori and Ohara in the first case, the λ-deformed estimators of Wong and Zhang in the second, both governed by a Pythagorean relation and reducing to maximum likelihood in the undeformed limit. We complete the picture with estimators drawn from the third connection the divergence defines, the Levi-Civita one, by minimizing geodesic distance: existence, uniqueness and influence are then controlled by curvature and diameter rather than by flatness. Along the way the Aitchison geometry appears as the logarithmic-barrier instance of the construction at its critical weight, and the symmetrization of the Pal-Wong transport cost, a smoothing of the Hilbert projective metric, is shown to lose the dual pair and to support instead a Huber-type robust center with bounded influence in a geometry of infinite diameter.