Comparación de la regresión GINI con la regresión de mínimos cuadrados ordinarios y otros modelos de regresión lineal robustos.

• Juan Carlos Correa Morales ^[1] ; Gloria Patricia Carmona Florez ^[1]
  1. [1] Universidad Nacional de Colombia

     Universidad Nacional de Colombia

     Colombia

     
• Localización: Comunicaciones en Estadística, ISSN 2027-3355, ISSN-e 2339-3076, Vol. 8, Nº. 2, 2015, págs. 129-161
• Idioma: español
• DOI: 10.15332/s2027-3355.2015.0002.01
• Títulos paralelos:
  • Comparison of Gini Regression with OLS Regression and other Robust Linear Regression.
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• Dialnet Métricas: 5 Citas
• Resumen
  • español

    En este trabajo se compara la regresión de Gini con la regresión OLS y otros métodos de regresión robustos, del tipo L (LAV, combinaciones lineales de estadísticos de orden), del tipo M (M de Huber, basado en el concepto de máxima verosimilitud) y del tipo MM (basado en la minimización de un estimador M). La comparación de los métodos se realiza vía simulación bajo diferentes escenarios. Los resultados encontrados vía simulación muestran que la regresión de Gini tiene un mayor grado de robustez en comparación con la regresíon OLS al estimar los coeficientes de regresión ante la presencia de datos atípicos, pero su robustez es menor que la de los métodos de estimación robustos LAV, M de Huber y MM.

  • English

    In this paper compares Gini regression (using the non-parametric approach of weighted average of slope, instead of the parametric approach) with OLS and other robust regression methods, the type L (LAV, linear combinations of order statistics), the type M (M Huber, based on the concept of maximum likelihood) and the type MM (based on the minimization of an estimator M). The comparison of the methods is performed via simulation under different scenarios. The results show that the Gini regression has a higher degree of robustness compared with the OLS regression to estimate the regression coefficients in the presence of outliers, but their robustness is less than robust estimation methods LAV, M Huber and MM.

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