Publication
Title
Transforming variables to central normality
Author
Abstract
Many real data sets contain numerical features (variables) whose distribution is far from normal (Gaussian). Instead, their distribution is often skewed. In order to handle such data it is customary to preprocess the variables to make them more normal. The Box–Cox and Yeo–Johnson transformations are well-known tools for this. However, the standard maximum likelihood estimator of their transformation parameter is highly sensitive to outliers, and will often try to move outliers inward at the expense of the normality of the central part of the data. We propose a modification of these transformations as well as an estimator of the transformation parameter that is robust to outliers, so the transformed data can be approximately normal in the center and a few outliers may deviate from it. It compares favorably to existing techniques in an extensive simulation study and on real data.
Language
English
Source (journal)
Machine learning. - Dordrecht
Publication
Dordrecht : 2021
ISSN
0885-6125
DOI
10.1007/S10994-021-05960-5
Volume/pages
(2021) , p. 1-32
Pubmed ID
000630995400003
Full text (Publisher's DOI)
Full text (open access)
UAntwerpen
Research group
Publication type
Subject
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Record
Identifier
Creation 27.02.2024
Last edited 28.02.2024
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