Outlier Detection Method in Linear Regression Based on Sum of Arithmetic Progression. (10th July 2014)

• Record Type: Journal Article
• Authors: Adikaram, K. K. L. B.; Hussein, M. A.; Effenberger, M.; Becker, T.
• Other Names: He Zengyou Academic Editor.
• Is Part Of: TheScientificWorldjournal. Volume 2014(2014)
• Publication Date: 2014-07-10
• Subjects: Science -- Periodicals; Technology -- Periodicals; Medicine -- Periodicals; 505
• DOI: 10.1155/2014/821623
• Languages: English
• ISSNs: 2356-6140

Abstract

Abstract : 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.