Combining some Biased Estimation Methods with Least Trimmed Squares Regression and its Application

• BETÜL KAN-KILINÇ ^[1] ; OZLEM ALPU ^[2]
  1. [1] Anadolu University

     Anadolu University

     Turquía

     
  2. [2] Eskişehir Osmangazi University

     Eskişehir Osmangazi University

     Turquía

     
• Localización: Revista Colombiana de Estadística, ISSN-e 2389-8976, ISSN 0120-1751, Vol. 38, Nº. 2, 2015, págs. 386-386
• Idioma: inglés
• DOI: 10.15446/rce.v38n2.51675
• Títulos paralelos:
  • Combinación de algunos métodos de estimación sesgados conregresión de mínimos cuadrados recortados y su aplicación
• Enlaces
  • Texto completo
• Resumen
  • español

    En el caso de multicolinealidad y outliers en análisis de regresión, los investigadores se enfrentan a tener que tratar dos problemas de manera simultánea. Métodos sesgados basados en estimadores robustos son útiles para estimar los coeficientes de regresión en estos casos. En este estudio se examinan algunos estimadores sesgados robustos en conjuntos de datos con outliers en x y outliers tanto en x como en y por medio del paquete ltsbase de R. En lugar de un análisis de datos completos, los estimadores sesgados robustos son evaluados usando las capacidades y características de este paquete.

  • English

    In the case of multicollinearity and outliers in regression analysis, the researchers are encouraged to deal with two problems simultaneously. Biased methods based on robust estimators are useful for estimating the regression coefficients for such cases. In this study we examine some robust biased estimators on the datasets with outliers in x direction and outliers in both x and y direction from literature by means of the R package ltsbase . Instead of a complete data analysis, robust biased estimators are evaluated using capabilities and features of this package.

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