# Outlier Detection under Interval Uncertainty: Algorithmic Solvability and Computational Complexity

Outlier Detection under Interval Uncertainty: Algorithmic Solvability and Computational Complexity 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 x 1, ...,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 k 0-sigma interval [E – k 0 ⋅ σ, E + k 0 ⋅ σ] (for some pre-selected parameter k 0).In real life,we often have only interval ranges [ ${\underline x}_i, {\bar x}_i$ ] for the normal values x 1, ...,x n . In this case,we only have intervals of possible values for the bounds $E - k_0 \cdot \sigma$ and $E + k_0 \cdot \sigma$ . We can therefore identify outliers as values that are outside all k 0-sigma intervals. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Reliable Computing Springer Journals

# Outlier Detection under Interval Uncertainty: Algorithmic Solvability and Computational Complexity

, Volume 11 (1) – Jan 1, 2005
18 pages

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Publisher
Kluwer Academic Publishers
Copyright
Copyright © 2005 by Springer Science + Business Media, Inc.
Subject
Mathematics; Numeric Computing; Approximations and Expansions; Computational Mathematics and Numerical Analysis; Mathematical Modeling and Industrial Mathematics
ISSN
1385-3139
eISSN
1573-1340
D.O.I.
10.1007/s11155-005-5943-7
Publisher site
See Article on Publisher Site

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