2014
DOI: 10.1080/03610918.2014.957839
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Estimating Box-Cox power transformation parameter via goodness-of-fit tests

Abstract: Box-Cox power transformation is a commonly used methodology to transform the distribution of a non-normal data into a normal one. Estimation of the transformation parameter is crucial in this methodology. In this study, the estimation process is hold via a searching algorithm and is integrated into wellknown seven goodness of fit tests for normal distribution. An artificial covariate method is also included for comparative purposes. Simulation studies are implemented to compare the effectiveness of the propose… Show more

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Cited by 51 publications
(33 citation statements)
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“…For all traits, the data were analyzed to test the assumptions for analysis of variance. For number of male and female flowers and number of fruits per plant, the assumption of homogeneity of residual variance was not met, requiring the transformation of the data using the Box-Cox method ( Asar et al, 2017 ). After the analysis of variance and determination of the significance of the variation sources, the treatments were grouped by the Scott-Knott test (p ≤ 0.05).…”
Section: Methodsmentioning
confidence: 99%
“…For all traits, the data were analyzed to test the assumptions for analysis of variance. For number of male and female flowers and number of fruits per plant, the assumption of homogeneity of residual variance was not met, requiring the transformation of the data using the Box-Cox method ( Asar et al, 2017 ). After the analysis of variance and determination of the significance of the variation sources, the treatments were grouped by the Scott-Knott test (p ≤ 0.05).…”
Section: Methodsmentioning
confidence: 99%
“…All statistical analyses were carried out using the R system for statistical computing, version 3.1.2 [33] with the packages lme4 [34], and AID [35]. Data exploration was done by visual inspection of outliers, homogeneity, and normality of residuals by means of Cleveland dotplots, conditional boxplots, and QQ-plots, respectively, as well as by using the boxcoxnc function of the AID package.…”
Section: Methodsmentioning
confidence: 99%
“…where y is the data to be transformed and λ is the transformation exponent. At the core of the Box-Cox transformation is an exponent, lambda (λ), which varies from − 5 to 5 (Asar et al 2017). By considering all values, the optimal value that resulted in the best approximation of a normal distribution curve for data was selected.…”
Section: Statisticsmentioning
confidence: 99%