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

Riani, Marco, Atkinson, Anthony C. and Corbellini, Aldo (2022) Automatic robust Box-Cox and extended Yeo-Johnson transformations in regression. Statistical Methods and Applications. ISSN 1618-2510

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Identification Number: 10.1007/s10260-022-00640-7

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.

Item Type: Article
Official URL: https://www.springer.com/journal/10260
Additional Information: © 2022 The Authors
Divisions: Statistics
Subjects: H Social Sciences > HA Statistics
Date Deposited: 21 Apr 2022 10:24
Last Modified: 27 Jun 2022 08:27
URI: http://eprints.lse.ac.uk/id/eprint/114903

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