Robust Statistical Engineering by Means of Scaled Bregman Distances

Anna-Lena Kißlinger () and Wolfgang Stummer ()
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Anna-Lena Kißlinger: University of Erlangen-Nürnberg, Chair of Statistics and Econometrics
Wolfgang Stummer: University of Erlangen-Nürnberg, Department of Mathematics

A chapter in Recent Advances in Robust Statistics: Theory and Applications, 2016, pp 81-113 from Springer

Abstract: Abstract We show how scaled Bregman distances can be used for the goal-oriented design of new outlier- and inlier robust statistical inference tools. Those extend several known distance-based robustness (respectively, stability) methods at once. Numerous special cases are illustrated, including 3D computer graphical comparison methods. For the discrete case, some universally applicable results on the asymptotics of the underlying scaled-Bregman-distance test statistics are derived as well.

Keywords: Probability Mass Function; Leibler Divergence; Hellinger Distance; Adjustment Function; Bregman Divergence (search for similar items in EconPapers)
Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-81-322-3643-6_5

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DOI: 10.1007/978-81-322-3643-6_5

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