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Estimación de parámetros en modelos de mezclas usando algoritmos evolutivos.

  • Natalia Romero-Rios [1] ; Juan Carlos Correa [1]
    1. [1] Universidad Nacional de Colombia

      Universidad Nacional de Colombia

      Colombia

  • Localización: Comunicaciones en Estadística, ISSN 2027-3355, ISSN-e 2339-3076, Vol. 9, Nº. 2, 2016, págs. 255-270
  • Idioma: español
  • DOI: 10.15332/s2027-3355.2016.0002.05
  • Títulos paralelos:
    • Parameter estimation in mixture models using evolutive algorithms.
  • Enlaces
  • Resumen
    • español

      Los modelos de mezclas son ampliamente usados en casos en los que se tienen elementos de poblaciones diversas, unidos en una super población. Hay métodos tradicionales para la estimación de los parámetros de modelos de mezclas, como lo son el bayesiano y el algoritmo de esperanza-maximización (EM). En esta investigación se propone usar los algoritmos evolutivos, como lo son los algoritmos genéticos, como método que puede servir para encontrar los parámetros de estimación de los modelos de mezclas. Para el desarrollo de este estudio se propone un algoritmo para la comparación de métodos evolutivos y tradicionales y se incluye un ejemplo de aplicación con datos reales. Se encontró que los algoritmos evolutivos son una opción competitiva para la estimación de parámetros en distribuciones de mezclas en los casos cuando las poblaciones en la mezcla siguen una distribución gamma, los pesos en las poblaciones son balanceados y el tamaño de muestra es mayor de 100 ítems. Para las mezclas de distribuciones normales y la estimación del número de poblaciones en una mezcla, el método tradicional es una mejor opción que el algoritmo genético

    • English

      The mixture models are widely used in cases when there are elements that come from different populations, mixed in a superpopulation. There are traditional methods for the estimation of the parameters in mixture models: the Bayesian Method and the Expectation-Maximization (EM) algorithm. For that reason, in this work we propose the use of evolutive algorithms, such as genetic algorithms. We propose an algorithm for the comparison of evolutive and traditional methods, and we illustrate the use of this algorithm with a real application. We found that the evolutive algorithms are a competitive option to estimate the parameters in mixture models in the cases when the populations in the mixture follows a gamma distribution, the weights of the populations in the mixture are even and the sample size is bigger than 100 items. For the mixture of normal distributions and the estimation of the number of populations in a mixture, the traditional method is a better option than the genetic algorithm.

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