Data Outlier Detection using the Chebyshev Theorem
Aerospace Conference IEEE, (2005)Abstract
During data collection and analysis, it is often necessary to identify
and possibly remove outliers that exist. An objective method for
identifying outliers to be removed is critical. Many automated outlier
detection methods are available. However, many are limited by assumptions
of a distribution or require upper and lower predefined boundaries
in which the data should exist. If there is a known distribution
for the data, then using that distribution can aid in finding outliers.
Often, a distribution is not known, or the experimenter does not
want to make an assumption about a certain distribution. Also, enough
information may not exist about a set of data to be able to determine
reliable upper and lower boundaries. For these cases, an outlier
detection method, using the empirical data and based upon Chebyshevâs
inequality, was formed. This method allows for detection of multiple
outliers, not just one at a time. This method also assumes that the
data are independent measurements and that a relatively small percentage
of outliers is contained in the data.