Robust correlation scaled principal component regression

In multiple regression, different techniques are available to deal with the situation where predictors large in number, and multicollinearity exists among them. Some of these approaches rely on correlation others depend principal components. To cope influential observations (outliers, leverage, or both) data matrix for regression purposes, two proposed this paper. These Robust Correlation Based Regression (RCBR) Scaled Principal Component (RCSPCR). methods compared existing methods, i.e., traditional (PCR), (CSPCR), (CBR). Also, Macro (Missingness cellwise row-wise outliers) RCSPCR is problem multicollinearity, high dimensionality dataset, outliers, missing simultaneously. The assessed by considering several simulated scenarios appropriate levels contamination. results indicate that suggested seem be more reliable analyzing missingness outlyingness. Additionally, real-life applications also used illustrate performance methods.