A decomposition approach for correlated random vector generation
"When doing simulation studies, it is necessary to consider correlation in input variables in order to obtain correct results. Generating correlated random nurnbers is not always an easy task, as the accuracy and computational stability of some methods depend of the probabilit, y distribution o...
- Autores:
-
Guaje Acosta, Oscar Orlando
- Tipo de recurso:
- Fecha de publicación:
- 2016
- Institución:
- Universidad de los Andes
- Repositorio:
- Séneca: repositorio Uniandes
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.uniandes.edu.co:1992/61040
- Acceso en línea:
- http://hdl.handle.net/1992/61040
- Palabra clave:
- Correlación (Estadística)
Métodos de simulación
Optimización matemática
- Rights
- openAccess
- License
- https://repositorio.uniandes.edu.co/static/pdf/aceptacion_uso_es.pdf
| Summary: | "When doing simulation studies, it is necessary to consider correlation in input variables in order to obtain correct results. Generating correlated random nurnbers is not always an easy task, as the accuracy and computational stability of some methods depend of the probabilit, y distribution of input variables. We present a method based on mixed-integer programrning to generate correlated random numbers. Since this method does not perform acceptably, we show a column generation procedure used to accelerate the MIP. We implemented our method and found significant computational improvements over the base MIP while improving the accuracy of the solution over known methods.".-- Tomado del resumen. |
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