A Non-Parametric Robust Estimation of the Box-Cox Transformation for Regression Models
AbstractIn regression analysis, it is frequently required to transform the dependent variable in order to obtain additivity and normal errors with constant variance. Box and Cox (1964) proposed a parametric power transformation based on the assumption of normality with the aim to achieve these goals. However, some authors such as Carroll (1980, 1982b), Bickel and Doksum (1981), Powell (1991), Chamberlain (1994), Buchinsky (1995), Marazzi and Yohai (2004) and Fitzenberger et al. (2005) have pointed out that this transformation is not robust to the presence of outliers, and propose robust estimators for the transformation parameter by replacing the normal likelihood with an objective function that is less sensitive to them. This paper presents a non-parametric alternative procedure for obtaining a power transformation within the Box-Cox family which is robust to the presence of outliers in the dependent variable. The procedure is an extension of the one proposed by Castaño (1994, 1995) for a symmetry transformation of a dataset.
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Bibliographic InfoArticle provided by Universidad de Antioquia, Departamento de Economía in its journal LECTURAS DE ECONOMÍA.
Volume (Year): (2011)
Issue (Month): 75 ()
Postal: Lecturas de Economía, Departamento de Economía, Calle 67, 53-108, Medellin 050010, Colombia.
Find related papers by JEL classification:
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
- C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
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