| bibtype |
J -
Journal Article
|
| ARLID |
0454495 |
| utime |
20240903202647.6 |
| mtime |
20160129235959.9 |
| SCOPUS |
84940989349 |
| title
(primary) (eng) |
Empirical estimates in stochastic programs with probability and second order stochastic dominance constraints |
| specification |
| page_count |
15 s. |
| media_type |
P |
|
| serial |
| ARLID |
cav_un_epca*0297096 |
| ISSN |
0862-9544 |
| title
|
Acta Mathematica Universitas Comenianae |
| volume_id |
84 |
| volume |
2 (2015) |
| page_num |
267-281 |
|
| keyword |
Stochastic programming problems |
| keyword |
empirical estimates |
| keyword |
light and heavy tailed distributions |
| keyword |
quantiles |
| author
(primary) |
| ARLID |
cav_un_auth*0271480 |
| full_dept (cz) |
Ekonometrie |
| full_dept (eng) |
Department of Econometrics |
| department (cz) |
E |
| department (eng) |
E |
| full_dept |
Department of Econometrics |
| name1 |
Omelchenko |
| name2 |
Vadym |
| institution |
UTIA-B |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0101122 |
| full_dept (cz) |
Ekonometrie |
| full_dept |
Department of Econometrics |
| department (cz) |
E |
| department |
E |
| full_dept |
Department of Econometrics |
| name1 |
Kaňková |
| name2 |
Vlasta |
| institution |
UTIA-B |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| source |
|
| cas_special |
| project |
| ARLID |
cav_un_auth*0292652 |
| project_id |
GA13-14445S |
| agency |
GA ČR |
|
| abstract
(eng) |
Stochastic optimization problems with an operator of the mathematical expectation in the objective function, probability and stochastic dominance constraints belong to “deterministic” problems depending on a probability measure. Complete knowledge of the probability measure is a necessary condition for solving these problems. However, since this assumption is very rarely fulfilled (in applications), problems are mostly solved on the basis of data. Mathematically it means that the “underlying” probability measure is replaced by an empirical one (determined by the corresponding data). Stochastic estimates of an optimal value and an optimal solution can only then be obtained. Properties of these estimates have been investigated many times, mostly in the case of constraint sets not depending on the probability measure. Our results generalize such estimates to two separate cases (already mentioned above) when the constraint sets do depend on the probability measure. |
| RIV |
BB |
| reportyear |
2016 |
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0255277 |
| confidential |
S |
| mrcbT16-s |
0.346 |
| mrcbT16-4 |
Q3 |
| mrcbT16-E |
Q3 |
| arlyear |
2015 |
| mrcbU14 |
84940989349 SCOPUS |
| mrcbU63 |
cav_un_epca*0297096 Acta Mathematica Universitas Comenianae 0862-9544 0862-9544 Roč. 84 č. 2 2015 267 281 |
|