Open AccessThe Redescending M estimator For detection and deletion of Outliers in Regression analysis
Author(s) -
Stella Anekwe,
Sidney I. Onyeagu
Publication year - 2021
Publication title -
pakistan journal of statistics and operation research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.354
H-Index - 15
eISSN - 2220-5810
pISSN - 1816-2711
DOI - 10.18187/pjsor.v17i4.3546
Subject(s) - outlier , ordinary least squares , mathematics , robust regression , estimator , statistics , least trimmed squares , robust statistics , regression analysis , regression , pattern recognition (psychology) , artificial intelligence , computer science , generalized least squares
Outliers in a statistical analysis strongly affect the performance of the ordinary least squares, such outliers need to be detected and extreme outliers deleted. Thisp is aimed at proposing a Redescending M-estimator which is more efficient and robust compared to other existing methods. The results show that the proposed method is effective in detection and deletion of extreme outliers compared to the other existing ones.