Outlier Detection Method in Linear Regression Based on Sum of Arithmetic Progression

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...

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Veröffentlicht in:TheScientificWorld Jg. 2014; H. 2014; S. 1 - 12
Hauptverfasser: Becker, T., Effenberger, M., Hussein, M. A., Adikaram, K. K. L. B.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Cairo, Egypt Hindawi Publishing Corporation 01.01.2014
John Wiley & Sons, Inc
Wiley
Schlagworte:
Algorithms
Biogas
Colleges & universities
Data Interpretation, Statistical
Electronic data processing
Genetic algorithms
Linear Models
Linear models (Statistics)
Linear regression models
Mathematical Concepts
Methods
Numerical Analysis, Computer-Assisted
Regression analysis
ISSN:2356-6140, 1537-744X, 1537-744X
Online-Zugang:Volltext
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Zusammenfassung: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.
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Academic Editor: Zengyou He
ISSN:2356-6140
1537-744X
1537-744X
DOI:10.1155/2014/821623