0498944 20240903170640.820190102235959.9 85064212406 000457070200005 10.14736/kyb-2018-6-1156 11 s. P cav_un_epca*02971630023-5954546 (2018)1156-1166Ústav teorie informace a automatizace AV ČR, v. v. i. optimization censored data empirical quantile cav_un_auth*0101227 Department of Stochastic Informatics 100 Volf Petr UTIA-B Stochastická informatika Department of Stochastic Informatics SI SI Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2019/SI/volf-0498944.pdf cav_un_auth*0363963 GA18-02739S GA ČR Stochastic optimization problem is, as a rule, formulated in terms of expected cost function. However, the criterion considered in the present contribution uses selected quantiles. Moreover, it is assumed that the stochastic characteristics of optimized system are estimated from the data, in a non-parametric setting, and that the data may be randomly right-censored. Therefore, certain theoretical results concerning estimators of distribution function and quantiles under censoring are recalled and then utilized to prove consistency of solution based on estimates. Behavior of solutions for fi nite data sizes is studied with the aid of randomly generated example of a newsvendor problem. WOS BB 10000 10100 10103 2019 1 RVO:67985556 http://hdl.handle.net/11104/0291525 S 3+4 Article|Proceedings Paper Computer Science Cybernetics COMPUTERSCIENCE.CYBERNETICS 0.591 0.155 13 0.00068 0.174 891 0.268 0.500 71 Q4 15.991 6.5 Q4 Q3 0.17 Q4 6.522 2018 85064212406 SCOPUS PUBMED 000457070200005 WOS cav_un_epca*0297163 Kybernetika 0023-5954 Roč. 54 č. 6 2018 1156 1166 Ústav teorie informace a automatizace AV ČR, v. v. i.