Afijación óptima de tamaños de muestra en muestreo aleatorio estratificado vía programación matemática
Somesamplingallocationproblemswhichcanbesolvedbymathematicalprogramming techniques are considered. Optimal allocation of sample sizes in Stratified Random Sampling (SI.), for example, can be regarded as a dynamic programming problem. In the multivariate case, the underlying convex–programming problem...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2010 |
| País: | Colombia |
| Institución: | Universidad Santo Tomás |
| Repositorio: | Repositorio Institucional USTA |
| Idioma: | español |
| OAI Identifier: | oai:repository.usta.edu.co:11634/39538 |
| Acceso en línea: | https://revistas.usantotomas.edu.co/index.php/estadistica/article/view/28 http://hdl.handle.net/11634/39538 |
| Access Level: | acceso abierto |
| Palabra clave: | Muestreo aleatorio estratificado Knapsack optimización programación matemática |
| Sumario: | Somesamplingallocationproblemswhichcanbesolvedbymathematicalprogramming techniques are considered. Optimal allocation of sample sizes in Stratified Random Sampling (SI.), for example, can be regarded as a dynamic programming problem. In the multivariate case, the underlying convex–programming problem is stated and some solution methods are indicated. We have followed the illuminating ideas exposed in Arthanari & Dodge (1981). Finally, an example to illustrate the so-called Knapsack method is presented. |
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