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Outlier Detection Method in Linear Regression Based on Sum of Arithmetic Progression
Joint Authors
Adikaram, K. K. L. B.
Becker, T.
Effenberger, M.
Hussein, M. A.
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-07-10
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
We introduce a new nonparametric outlier detection method for linear series, which requires no missing or removed data imputation.
For an arithmetic progression (a series without outliers) with n elements, the ratio ( R ) of the sum of the minimum and the maximum elements and the sum of all elements is always 2 / n : ( 0,1 ] .
R ≠ 2 / n always implies the existence of outliers.
Usually, R < 2 / n implies that the minimum is an outlier, and R > 2 / n implies that the maximum is an outlier.
Based upon this, we derived a new method for identifying significant and nonsignificant outliers, separately.
Two different techniques were used to manage missing data and removed outliers: (1) recalculate the terms after (or before) the removed or missing element while maintaining the initial angle in relation to a certain point or (2) transform data into a constant value, which is not affected by missing or removed elements.
With a reference element, which was not an outlier, the method detected all outliers from data sets with 6 to 1000 elements containing 50% outliers which deviated by a factor of ± 1.0 e - 2 to ± 1.0 e + 2 from the correct value.
American Psychological Association (APA)
Adikaram, K. K. L. B.& Hussein, M. A.& Effenberger, M.& Becker, T.. 2014. Outlier Detection Method in Linear Regression Based on Sum of Arithmetic Progression. The Scientific World Journal،Vol. 2014, no. 2014, pp.1-12.
https://search.emarefa.net/detail/BIM-1051199
Modern Language Association (MLA)
Adikaram, K. K. L. B.…[et al.]. Outlier Detection Method in Linear Regression Based on Sum of Arithmetic Progression. The Scientific World Journal No. 2014 (2014), pp.1-12.
https://search.emarefa.net/detail/BIM-1051199
American Medical Association (AMA)
Adikaram, K. K. L. B.& Hussein, M. A.& Effenberger, M.& Becker, T.. Outlier Detection Method in Linear Regression Based on Sum of Arithmetic Progression. The Scientific World Journal. 2014. Vol. 2014, no. 2014, pp.1-12.
https://search.emarefa.net/detail/BIM-1051199
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1051199