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A methodology to implement Box-Cox transformation when no covariate is available

Research output: Contribution to journalJournal articlepeer-review

Published
  • Osman Dag
  • Özgür Asar
  • Ozlem Ilk
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<mark>Journal publication date</mark>2014
<mark>Journal</mark>Communications in Statistics – Simulation and Computation
Issue number7
Volume43
Number of pages20
Pages (from-to)1740-1759
Publication StatusPublished
Early online date9/08/13
<mark>Original language</mark>English

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 paper, 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 data sets.