A Methodology to Implement Box-Cox Transformation When No Covariate is Available |
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Authors: | Osman Dag Ozgur Asar Ozlem Ilk |
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Affiliation: | Department of Statistics, Faculty of Arts and Sciences , Middle East Technical University , Ankara , Turkey |
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Abstract: | Box-Cox transformation is one of the most commonly used methodologies when data do not follow normal distribution. However, its use is restricted since it usually requires the availability of covariates. In this article, the use of a non-informative auxiliary variable is proposed for the implementation of Box-Cox transformation. Simulation studies are conducted to illustrate that the proposed approach is successful in attaining normality under different sample sizes and most of the distributions and in estimating transformation parameter for different sample sizes and mean-variance combinations. Methodology is illustrated on two real-life datasets. |
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Keywords: | Data transformation Maximum likelihood estimation Non-informative covariate Normality Regression analysis Statistical distributions |
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