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

/lp/springer_journal/outlier-detection-under-interval-uncertainty-algorithmic-solvability-iuDty5kXSd
Publisher
Springer Journals
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

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 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.

Journal

Reliable ComputingSpringer Journals

Published: Jan 1, 2005

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