Abstract
Outlying 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 normalization and high-level statistical analysis. This straightforward univariate transformation identifies extreme values and reduces the influence of outlying values considerably in all further steps of statistical analysis without eliminating the incriminated observation or feature. The use of the transformation and its effects are demonstrated for diverse univariate and multivariate statistical analyses using nine publicly available microarray data sets.
Dokumententyp: | Paper |
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Publikationsform: | Submitted Version |
Fakultät: | Mathematik, Informatik und Statistik > Statistik > Technische Reports |
Themengebiete: | 500 Naturwissenschaften und Mathematik > 510 Mathematik |
URN: | urn:nbn:de:bvb:19-epub-11501-2 |
Sprache: | Englisch |
Dokumenten ID: | 11501 |
Datum der Veröffentlichung auf Open Access LMU: | 06. Mai 2010, 07:53 |
Letzte Änderungen: | 04. Nov. 2020, 12:52 |