False Discovery versus Familywise Error Rate Approaches to Outlier Detection

This study deals with outlier detection approaches for large samples from continuous univariate distributions. Investigated are the properties of a practical newer outlier detection approach based on use of a false discovery rate method in conjunction with a robustly estimated Tukey g-and-h base distribution.

Compared are the properties of a boxplot type outlier detection approach that controls the familywise error rate with a newer false discovery rate approach. These options are compared in terms of error rates and effects of moving the outliers gradually further from base distribution center while using 5% and 1% false discovery rate or familywise error rates. Two microarray data sets are used as examples where the assumed null distributions do not fit the data well.