An algorithm for estimating Box–Cox transformation parameter in ANOVA

Date

2016-8-5

Author

Dag, Osman
İlk Dağ, Özlem

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In this study, we construct a feasible region, in which we maximize the likelihood function, by using Shapiro-Wilk and Bartlett's test statistics to obtain Box-Cox power transformation parameter for solving the issues of non-normality and/or heterogeneity of variances in analysis of variance (ANOVA). Simulation studies illustrate that the proposed approach is more successful in attaining normality and variance stabilization, and is at least as good as the usual maximum likelihood estimation (MLE) in estimating the transformation parameter for different conditions. Our proposed method is illustrated on two real-life datasets. Moreover, the proposed algorithm is released under R package AID under the name of "boxcoxfr" for implementation.

Subject Keywords

Data transformation, Homogeneity test, Normality test, Searching algorithms, Statistical software

URI

https://hdl.handle.net/11511/28336

Journal

Communications in Statistics - Simulation and Computation

DOI

https://doi.org/10.1080/03610918.2016.1204458

Collections

Department of Statistics, Article

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Citation Formats

O. Dag and Ö. İlk Dağ, “An algorithm for estimating Box–Cox transformation parameter in ANOVA,” Communications in Statistics - Simulation and Computation, pp. 6424–6435, 2016, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/28336.