Supplementary material to the paper

 

Automatic Robust Box-Cox and Extended Yeo-Johnson Transformations

 

Marco Riani  Anthony C. Atkinson Aldo Corbellini
Department of Economics and Managemenet and Interdepartmental Centre for Robust Statistics The London School of Economics, Department of Economics and Managemenet and Interdepartmental Centre for Robust Statistics
University of Parma London WC2A 2AE, UK University of Parma
Italy  UK Italy
mriani@unipr.it  a.c.atkinson@lse.ac.uk aldo.corbellini@unipr.it

Abstract


The paper introduces an automatic procedure for the transformation of the response in regression models to approximate normality. Because incorrectly transformed response can generate spurious outliers, robustness is essential. We consider the Box-Cox transformation and its generalization to the extended Yeo-Johnson transformation which allows for responses of both signs. The usefulness of our automatic procedure is demonstrated on four sets of data. An important theoretical development is an extension of the Bayesian Information Criterion (BIC) to allow for the comparison of models following the deletion of observations, the number deleted depending on the transformation parameter.

 

Data used in the paper are included in release 2020A of the FSDA toolbox downloadable from

Mathworks file exchange

https://www.mathworks.com/matlabcentral/fileexchange/72999-fsda

or from github repo

https://github.com/UniprJRC/FSDA

 

Matlab code used in the paper

 

The matlab file which creates all the figures  can be downloaded here

 


Last modified 27/08/2020 11.05.45