PERBANDINGAN REGRESI KOMPONEN UTAMA DAN ROBPCA DALAM MENGATASI MULTIKOLINEARITAS DAN PENCILAN PADA REGRESI LINEAR BERGANDA

Multiple linear regression analysis with a lot of independent variable always makes many problems because there is a relationship between two or more independent variables. The independent variables which correlated each other are called multicollinearity. Principal component  analysis which based on variance covariance matrix is very sensitive toward the existence of outlier in the observing data. Therefore in order to overcome the problem of outlier it is needed a method of robust estimator toward outlier. ROBPCA is a robust method for PCA toward the existence of outlier in the data. In order to obtain the robust principal component is needed a combination of Projection Pursuit (PP) with Minimum Covariant Determinant (MCD). The results showed that the ROBPCA method has a bias parameter and Mean Square Error (MSE) parameter lower than Principal Component Regression method. This case shows that the ROBPCA method better cope with the multicollinearity observational data influenced by outlier.