A Methodology to Implement Box-Cox Transformation When No Covariate is Available

Date

2014-01-01

Author

Dag, Osman
Asar, Ozgur
İlk Dağ, Özlem

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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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.

Subject Keywords

Statistical distributions, Regression analysis, Normality, Non-informative covariate, Maximum likelihood estimation, Data transformation

URI

https://hdl.handle.net/11511/32826

Journal

COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION

DOI

https://doi.org/10.1080/03610918.2012.744042

Collections

Department of Statistics, Article

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Citation Formats

O. Dag, O. Asar, and Ö. İlk Dağ, “A Methodology to Implement Box-Cox Transformation When No Covariate is Available,” COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, pp. 1740–1759, 2014, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/32826.