A new toolkit for robust distributional change detection

Anna‐Lena Kißlinger and Wolfgang Stummer

Applied Stochastic Models in Business and Industry, 2018, vol. 34, issue 5, 682-699

Abstract: Divergences (distances), which measure the dissimilarity, respectively, proximity, between two probability distributions, have turned out to be very useful for several different tasks in statistics (eg, parameter estimation and goodness‐of‐fit testing), econometrics, machine learning, information theory, etc. Some prominent examples are the Kullback‐Leibler information (relative entropy), the Csiszár‐Ali‐Silvey ϕ‐divergences, the “ordinary” (ie, unscaled) Bregman divergences, and the recently developed more general scaled Bregman divergences. Out of the latter and a novel extension to nonconvex generators, we form a new toolkit for detecting distributional changes in random data (streams and clouds). Some sample‐size asymptotics is investigated as well.

Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:wly:apsmbi:v:34:y:2018:i:5:p:682-699

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