Premium
Use of pretransformation to cope with extreme values in important candidate features
Author(s) -
Boulesteix AnneLaure,
Guillemot Vincent,
Sauerbrei Willi
Publication year - 2011
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/bimj.201000189
Subject(s) - univariate , outlier , context (archaeology) , extreme value theory , preprocessor , multivariate statistics , transformation (genetics) , computer science , data mining , data transformation , statistics , mathematics , artificial intelligence , biology , paleontology , biochemistry , gene , data warehouse
Extreme values in predictors often strongly affect the results of statistical analyses in high‐dimensional settings. Although they frequently occur with most high‐throughput techniques, the problem is often ignored in the literature. We suggest to use a very simple transformation, proposed before in a different context by Royston and Sauerbrei, as an intermediary step between array preprocessing and high‐level statistical analysis. This straightforward univariate transformation identifies extreme values in continuous features and can thus be used as a diagnostic tool for outliers. The use of the transformation and its effects is demonstrated for diverse univariate and multivariate statistical analyses using nine publicly available microarray data sets.