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

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A methodology to implement Box-Cox transformation when no covariate is available. / Dag, Osman; Asar, Özgür; Ilk, Ozlem.
In: Communications in Statistics – Simulation and Computation, Vol. 43, No. 7, 2014, p. 1740-1759.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Dag, O, Asar, Ö & Ilk, O 2014, 'A methodology to implement Box-Cox transformation when no covariate is available', Communications in Statistics – Simulation and Computation, vol. 43, no. 7, pp. 1740-1759. https://doi.org/10.1080/03610918.2012.744042

APA

Dag, O., Asar, Ö., & Ilk, O. (2014). A methodology to implement Box-Cox transformation when no covariate is available. Communications in Statistics – Simulation and Computation, 43(7), 1740-1759. https://doi.org/10.1080/03610918.2012.744042

Vancouver

Dag O, Asar Ö, Ilk O. A methodology to implement Box-Cox transformation when no covariate is available. Communications in Statistics – Simulation and Computation. 2014;43(7):1740-1759. Epub 2013 Aug 9. doi: 10.1080/03610918.2012.744042

Author

Dag, Osman ; Asar, Özgür ; Ilk, Ozlem. / A methodology to implement Box-Cox transformation when no covariate is available. In: Communications in Statistics – Simulation and Computation. 2014 ; Vol. 43, No. 7. pp. 1740-1759.

Bibtex

@article{9d4972de257748b480c3f46a88413119,

title = "A methodology to implement Box-Cox transformation when no covariate is available",

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.",

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

author = "Osman Dag and {\"O}zg{\"u}r Asar and Ozlem Ilk",

year = "2014",

doi = "10.1080/03610918.2012.744042",

language = "English",

volume = "43",

pages = "1740--1759",

journal = "Communications in Statistics – Simulation and Computation",

issn = "0361-0918",

publisher = "Taylor and Francis Ltd.",

number = "7",

}

RIS

TY - JOUR

T1 - A methodology to implement Box-Cox transformation when no covariate is available

AU - Dag, Osman

AU - Asar, Özgür

AU - Ilk, Ozlem

PY - 2014

Y1 - 2014

N2 - 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.

AB - 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.

KW - Normality

KW - Data transformation

KW - Regression analysis

KW - Non-informative covariate

KW - Statistical distributions

KW - Maximum likelihood estimation

U2 - 10.1080/03610918.2012.744042

DO - 10.1080/03610918.2012.744042

M3 - Journal article

VL - 43

SP - 1740

EP - 1759

JO - Communications in Statistics – Simulation and Computation

JF - Communications in Statistics – Simulation and Computation

SN - 0361-0918

IS - 7

ER -

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