The paper introduces an automatic procedure for the parametric transformation of the response in regression models to approximate normality. We consider the Box–Cox transformation and its generalization to the extended Yeo–Johnson transformation which allows for both positive and negative responses. A simulation study illuminates the superior comparative properties of our automatic procedure for the Box–Cox transformation. The usefulness of our procedure is demonstrated on four sets of data, two including negative observations. An important theoretical development is an extension of the Bayesian Information Criterion (BIC) to the comparison of models following the deletion of observations, the number deleted here depending on the transformation parameter.

Automatic robust Box–Cox and extended Yeo–Johnson transformations in regression / Riani, M.; Atkinson, A. C.; Corbellini, A.. - In: STATISTICAL METHODS & APPLICATIONS. - ISSN 1618-2510. - (2022). [10.1007/s10260-022-00640-7]

Automatic robust Box–Cox and extended Yeo–Johnson transformations in regression

Riani M.;Atkinson A. C.
;
Corbellini A.
2022-01-01

Abstract

The paper introduces an automatic procedure for the parametric transformation of the response in regression models to approximate normality. We consider the Box–Cox transformation and its generalization to the extended Yeo–Johnson transformation which allows for both positive and negative responses. A simulation study illuminates the superior comparative properties of our automatic procedure for the Box–Cox transformation. The usefulness of our procedure is demonstrated on four sets of data, two including negative observations. An important theoretical development is an extension of the Bayesian Information Criterion (BIC) to the comparison of models following the deletion of observations, the number deleted here depending on the transformation parameter.
Automatic robust Box–Cox and extended Yeo–Johnson transformations in regression / Riani, M.; Atkinson, A. C.; Corbellini, A.. - In: STATISTICAL METHODS & APPLICATIONS. - ISSN 1618-2510. - (2022). [10.1007/s10260-022-00640-7]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11381/2933995
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