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Statistical inference and hypotheses testing of risk averse stochastic programs

Vincent Guigues (vincent.guigues***at***gmail.com)
Alexander Shapiro (ashapiro***at***isye.gatech.edu)
Volker Kratschmer (volker.kraetschmer***at***uni-due.de)

Abstract: We study statistical properties of the optimal value and optimal solutions of the Sample Average Approximation of risk averse stochastic problems. Central Limit Theorem type results are derived for the optimal value when the stochastic program is expressed in terms of a law invariant coherent risk measure having a discrete Kusuoka representation. The obtained results are applied to hypotheses testing problems aiming at comparing the optimal values of several risk averse convex stochastic programs on the basis of samples of the underlying random vectors. We also consider non-asymptotic tests based on confidence intervals on the optimal values of the stochastic programs obtained using the Stochastic Mirror Descent algorithm. Numerical simulations show how to use our developments to choose among different distributions and show the superiority of the asymptotic tests on a class of risk averse stochastic programs.

Keywords: Stochastic optimization, Sample Average Approximation, hypotheses testing, coherent risk measures, statistical inference, Central Limit Theorem

Category 1: Stochastic Programming


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Entry Submitted: 11/16/2016
Entry Accepted: 11/16/2016
Entry Last Modified: 01/23/2018

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