Abstract
In many application areas, it is important to detect outliers. The traditional engineering approach to outlier detection is that we start with some “normal” values x1,..., x n , compute the sample average E, the sample standard variation σ, and then mark a value x as an outlier if x is outside the k0-sigma interval [E – k0 · σ, E + k0 · σ] (for some pre-selected parameter k0). In real life, we often have only interval ranges [\(\underline{x}_{i}\), \(\overline{x}_{i}\)] for the normal values x1,..., x n . In this case, we only have intervals of possible values for the “outlier threshold” – bounds E – k0 · σ and E + k0 · σ. We can therefore identify outliers as values that are outside all k0-sigma intervals.
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© 2012 Springer-Verlag Berlin Heidelberg
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Nguyen, H.T., Kreinovich, V., Wu, B., Xiang, G. (2012). Computing Outlier Thresholds under Interval Uncertainty. In: Computing Statistics under Interval and Fuzzy Uncertainty. Studies in Computational Intelligence, vol 393. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24905-1_18
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DOI: https://doi.org/10.1007/978-3-642-24905-1_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24904-4
Online ISBN: 978-3-642-24905-1
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