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Mathematical Programming

, Volume 174, Issue 1–2, pp 225–251 | Cite as

Statistics with set-valued functions: applications to inverse approximate optimization

  • Anil AswaniEmail author
Full Length Paper Series B
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Abstract

Much of statistics relies upon four key elements: a law of large numbers, a calculus to operationalize stochastic convergence, a central limit theorem, and a framework for constructing local approximations. These elements are well-understood for objects in a vector space (e.g., points or functions); however, much statistical theory does not directly translate to sets because they do not form a vector space. Building on probability theory for random sets, this paper uses variational analysis to develop operational tools for statistics with set-valued functions. These tools are first applied to nonparametric estimation (kernel regression of set-valued functions). The second application is to the problem of inverse approximate optimization, in which approximate solutions (corrupted by noise) to an optimization problem are observed and then used to estimate the amount of suboptimality of the solutions and the parameters of the optimization problem that generated the solutions. We show that previous approaches to this problem are statistically inconsistent when the data is corrupted by noise, whereas our approach is consistent under mild conditions.

Keywords

Set-valued functions Statistics Inverse optimization 

Mathematics Subject Classification

62F12 62G08 49J53 

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature and Mathematical Optimization Society 2018

Authors and Affiliations

  1. 1.Industrial Engineering and Operations ResearchUniversity of CaliforniaBerkeleyUSA

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