Open AccessBreakdown Analysis of Pearson Correlation Coefficient and Robust Correlation Methods
Author(s) -
Friday Zinzendoff Okwonu,
Bolaji Laro Asaju,
Festus Irimisose Arunaye
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/917/1/012065
Subject(s) - pearson product moment correlation coefficient , robustness (evolution) , correlation coefficient , correlation , statistics , moment (physics) , data correlation , data set , spark plug , robust statistics , computer science , mathematics , data mining , physics , chemistry , thermodynamics , biochemistry , geometry , classical mechanics , gene , estimator
This paper discussed plug in robust procedure to robustify the Pearson product moment correlation coefficient (PPMCC). The mean of PPMCC is highly susceptible to influential observations hence the PPMCC is not robust against data set that contains substantial amount of influential observations. This study focused on robust plug in techniques with high breakdown points. The performance of these techniques are compared using real and simulated data set. The comparative analysis indicates different degrees of robustness and breakdown based on the percentage of contamination and data modification.