caa a22 4500 606233148 CHVBK 20210128101230.0 cr unu---uuuuu 210128e20150401xx s 000 0 eng 10.1007/s10287-014-0225-7 doi (NATIONALLICENCE)springer-10.1007/s10287-014-0225-7 Kozmík Václav Department of Probability and Mathematical Statistics, Faculty of Mathematics and Physics, Charles University in Prague, Prague, Czech Republic aut On variance reduction of mean-CVaR Monte Carlo estimators [Elektronische Daten] [Václav Kozmík] We formulate an objective as a convex combination of expectation and risk, measured by the $$\mathrm{CVaR }$$ CVaR risk measure. The poor performance of standard Monte Carlo estimators applied on functions of this form is discussed and a variance reduction scheme based on importance sampling is proposed. We provide analytical solution for random variables based on normal distribution and outline the way for the other distributions, either by analytical computation or by sampling. Our results are applied in the framework of stochastic dual dynamic programming algorithm. Computational results which validate the previous analysis are given. Springer-Verlag Berlin Heidelberg, 2014 Importance sampling nationallicence Risk-averse optimization nationallicence Monte Carlo sampling nationallicence Stochastic dual dynamic programming nationallicence Computational Management Science Springer Berlin Heidelberg 12/2(2015-04-01), 221-242 1619-697X 12:2<221 2015 12 10287 https://doi.org/10.1007/s10287-014-0225-7 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s10287-014-0225-7 text/html Onlinezugriff via DOI NATIONALLICENCE 100 1- Kozmík Václav Department of Probability and Mathematical Statistics, Faculty of Mathematics and Physics, Charles University in Prague, Prague, Czech Republic aut NATIONALLICENCE 773 0- Computational Management Science Springer Berlin Heidelberg 12/2(2015-04-01), 221-242 1619-697X 12:2<221 2015 12 10287