Analisis Regresi Komponen Utama Robust dengan Metode Minimum Covariance Determinant – Least Trimmed Square (MCD-LTS)

Siska Diah Ayu Larasati, Khoirin Nisa, Eri Setiawan


Principal Component Regression (PCR) is a method used to overcome multicollinearity problems by reducing the dimensions of independent variables to obtain new simpler variables without losing most of the information contained in the  variables. If the data analyzed contain outliers, a robust method on PCR is required. In this paper we use a robust method which is a combination of Robust Principal Component Analysis using the Minimum Covariance Determinant (MCD) method and Robust Regression Analysis using Least Trimmed Square (LTS) method. The purpose of this study is to examine the robust PCR analysis using the MCD-LTS method and to know the robustness of the method by looking at its sensitivity to outliers. For this purpose  we compared the MCD-LTS PCR  to the classic PCR based on the bias and Mean Square Error (MSE) values on several different sample sizes and percentages of outliers. The results of this study indicate that robust PCR using MCD-LTS is effective and efficient in overcoming the problem of multicollinearity and outliers in regression analysis.



multicollinearity; outliers; principal component regression; robust

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