TITLE:
Outlier Detection and Effects on Modeling
AUTHORS:
Christopher O. Arimie, Emmanuel O. Biu, Maxwell A. Ijomah
KEYWORDS:
Outliers’ Detection, Classification and Comparisons, Simple and Multiple Linear Regression Models
JOURNAL NAME:
Open Access Library Journal,
Vol.7 No.9,
September
16,
2020
ABSTRACT:
In this work, a comprehensive framework for traditional outlier detection techniques based on simple and multiple linear regression models was studied. Two data sets were used for the illustration and evaluation of each class of outlier detection techniques (analytical and graphical methods). Outlier detection aims at identifying such outlier in order to improve the analytic of data and suitable model built. Furthermore, comparisons of the different methods were done to highlight the advantages, disadvantages and performance issues of each class of outlier detection techniques. The results show that by removing the influential points (or outliers), the model adequacy increased (from R2 = 0.72 to R2 = 0.97). It was observed that Jackknife residuals and Atkinson’s measure methods are very useful in detecting outliers; hence, both methods were recommended for outliers’ detection.