Scenario generation with distribution functions and correlations
Kybernetika (2014)
- Volume: 50, Issue: 6, page 1049-1064
- ISSN: 0023-5954
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topKaut, Michal, and Lium, Arnt-Gunnar. "Scenario generation with distribution functions and correlations." Kybernetika 50.6 (2014): 1049-1064. <http://eudml.org/doc/262201>.
@article{Kaut2014,
abstract = {In this paper, we present a method for generating scenarios for two-stage stochastic programs, using multivariate distributions specified by their marginal distributions and the correlation matrix. The margins are described by their cumulative distribution functions and we allow each margin to be of different type. We demonstrate the method on a model from stochastic service network design and show that it improves the stability of the scenario-generation process, compared to both sampling and a method that matches moments and correlations.},
author = {Kaut, Michal, Lium, Arnt-Gunnar},
journal = {Kybernetika},
keywords = {stochastic programming; scenario generation; moment matching; distribution functions; service network design; stochastic programming; scenario generation; moment matching; distribution functions; service network design},
language = {eng},
number = {6},
pages = {1049-1064},
publisher = {Institute of Information Theory and Automation AS CR},
title = {Scenario generation with distribution functions and correlations},
url = {http://eudml.org/doc/262201},
volume = {50},
year = {2014},
}
TY - JOUR
AU - Kaut, Michal
AU - Lium, Arnt-Gunnar
TI - Scenario generation with distribution functions and correlations
JO - Kybernetika
PY - 2014
PB - Institute of Information Theory and Automation AS CR
VL - 50
IS - 6
SP - 1049
EP - 1064
AB - In this paper, we present a method for generating scenarios for two-stage stochastic programs, using multivariate distributions specified by their marginal distributions and the correlation matrix. The margins are described by their cumulative distribution functions and we allow each margin to be of different type. We demonstrate the method on a model from stochastic service network design and show that it improves the stability of the scenario-generation process, compared to both sampling and a method that matches moments and correlations.
LA - eng
KW - stochastic programming; scenario generation; moment matching; distribution functions; service network design; stochastic programming; scenario generation; moment matching; distribution functions; service network design
UR - http://eudml.org/doc/262201
ER -
References
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