Geometrical Bounds for Variance and Recentered Moments

Tongseok Lim () and Robert J. McCann ()
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Tongseok Lim: Krannert School of Management, Purdue University, West Lafayette, Indiana 47907
Robert J. McCann: Department of Mathematics, University of Toronto, Toronto, Ontario M5S 2E4, Canada

Mathematics of Operations Research, 2022, vol. 47, issue 1, 286-296

Abstract: We bound the variance and other moments of a random vector based on the range of its realizations, thus generalizing inequalities of Popoviciu and of Bhatia and Davis concerning measures on the line to several dimensions. This is done using convex duality and (infinite-dimensional) linear programming. The following consequence of our bounds exhibits symmetry breaking, provides a new proof of Jung’s theorem, and turns out to have applications to the aggregation dynamics modelling attractive–repulsive interactions: among probability measures on R n whose support has diameter at most 2 , we show that the variance around the mean is maximized precisely by those measures that assign mass 1 / ( n + 1 ) to each vertex of a standard simplex. For 1 ≤ p < ∞ , the p th moment—optimally centered—is maximized by the same measures among those satisfying the diameter constraint.

Keywords: Primary: 62H05; secondary: 49N15; 52A40; 60E15; 90C46; multidimensional moment bounds; random vectors; convex duality; infinite-dimensional linear programming; variance; Popoviciu; Bhatia; Davis; Jung; Legendre–Fenchel; isodiametric inequality (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormoor:v:47:y:2022:i:1:p:286-296

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