A CENTRAL LIMIT THEOREM AND HYPOTHESES TESTING FOR RISK-AVERSE STOCHASTIC PROGRAMS

作者：Cuicues Vincent^*; Kraetschmer Volker; Shapiro Alexander

来源：SIAM Journal on Optimization, 2018, 28(2): 1337-1366.

DOI：10.1137/16M1104639

• stochastic optimization
• sample average approximation
• hypotheses testing
• coherent risk measures
• statistical inference
• central limit theorem

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 nonasymptotic 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 on the considered class of risk-averse stochastic programs the asymptotic tests show better results.

• 出版日期2018