The Ueda's method is an outlier detection method that focuses on the symmetry and skewness of the distribution in order to detect outlying data points.
Thus, although the Ueda's method is an outlier detection method, it does perform like a symmetry test in some cases.
Then, the GESD-based method filters out an outlier by applying the assumption that clock offsets from benign nodes follow the same (or similar) distribution or pattern.
In particular, our method relies on an outlier removal step, which effectively reduces the variance and sample size.
Another important criterion for selection of an outlier removal method was the ability of a method to be performed without any additional user interaction.
To build this map an outlier removal method is implemented and a statistical approach used, so that a simplified metric map can be obtained for robot navigation.
This can be done either by modeling the impulsive noise with a non-Gaussian distribution such as the Symmetric α-stable (SαS) distribution or by applying an outlier detection method.
Additional filtering was applied to remove cases with excessive CNV counts, and a threshold of >72 CNVs per case was established using an outlier detection method for skewed data.
Indeed, we observed few outliers in two datasets (there were 31 outliers in Dataset 14 and 7 outliers in Dataset 29; they corresponded to (31 + 7)/(22,283 clones × 36 datasets) = 0.0047%) when an outlier detection method based on Akaike's Information Criterion (AIC) [ 85- 87] was applied to the average expression vector (x 1 ¯,..., x p ¯ ) calculated from each of the 36 datasets.
Overall, the GTI seemed to perform best in comparison to the other methods in identifying genes with an outlier profile among the glioma large-scale integrated dataset.
