Print ISSN: 1680-855X

Online ISSN: 2664-2956

Keywords : Multi-Collinearity

Robust Weighted Approaches to Detect and Deal with Outliers in Estimating Principal Component Regression Model

Esraa Najeeb Alsaraf; Bashar Abdulaziz AL-Talib

IRAQI JOURNAL OF STATISTICAL SCIENCES, 2021, Volume 18, Issue 33, Pages 1-21
DOI: 10.33899/iqjoss.2021.168371

This paper aims to propose an approach to deal with the problem of Multi-Collinearity between the explanatory variables and outliers in the data by using the method of Principal Component Regression, and then using a robust weighting functions for the objective function has been used to deal with the presence of outliers in the data, and in order to verify the efficiency of the estimators, an experimental study was conducted through the simulation approach, and the methods were also applied to real data collected from the files of Badoush Cement Factory in Nineveh Governorate for the period from (2008-2014) with nine explanatory variables representing the chemical properties of cement and a dependent variable representing the physical properties of cement (hardness).
The data was tested whether it was suffer from multi-collinearity problem and then the least squares using principal components as an explanatory variables and the model was estimated, and it was found that the variables suffer from Multi-Collinearity problem, and the treatment was done by applying principal component regression weighed by robust weights due to the presence of outlying values in the data in addition to the collinearity problem.