Volker Krätschmer – Optimization Online

Statistical inference and hypotheses testing of risk averse stochastic programs

Published: 2016/11/16, Updated: 2018/01/23

Stochastic Programming central limit theorem, coherent risk measures, hypotheses testing, sample average approximation, statistical inference, stochastic optimization

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 … Read more

Statistical inference and hypotheses testing of risk averse stochastic programs

Published: 2016/03/23

Vincent Guigues

Alexander Shapiro

Volker Krätschmer

Statistics, Stochastic Programming central limit theorem, coherent risk measure, hypotheses testing, sample average approximation, statistical inference, stochastic optimization

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 and optimal solutions when the stochastic program is expressed in terms of a law invariant coherent risk measure. The obtained results are applied … Read more