PERBANDINGAN METODE ROBUST MCD-LMS, MCD-LTS, MVE-LMS, DAN MVE-LTS DALAM ANALISIS REGRESI KOMPONEN UTAMA

Sekar Wulandari
Nur Salam
Dewi Anggraini

Epsilon: Jurnal Matematika Murni dan Terapan
Vol 4, No 1 (2010): JURNAL EPSILON VOLUME 4 NOMOR 1

Principal Component Regression (PCR) is one of the widely used statistical techniques forregression analysis with colinearity. A robust technique on CR required is when data containsoutlier is urgently needed.In this research we consider combination between Robust Principal Ccomponent Analysis(PCA): Minimum Covariance Determinant (MCD) and Minimum Volume Ellipsoid (MVE) withRobust Regression methods: Least Median Square (LMS), and Least Trimmed Square (LTS), thencompare resistance level of MCD-LMS, MCD-LTS, MVE-LMS and MVE-LTS through the biasand the mean square error on some samples size and outlier’s percentage.The result shows that the MCD-LMS perform better than MCD-LTS, MVE-LMS, and MCDLTS.