PENERAPAN METODE LEAST MEDIAN SQUARE-MINIMUM COVARIANCE DETERMINANT (LMS-MCD) DALAM REGRESI KOMPONEN UTAMA

• I PUTU EKA IRAWAN Faculty of Mathematics and Natural Sciences, Udayana University
• I KOMANG GDE SUKARSA Faculty of Mathematics and Natural Sciences, Udayana University
• NI MADE ASIH Faculty of Mathematics and Natural Sciences, Udayana University

Abstract

Principal Component Regression is a method to overcome multicollinearity techniques by combining principal component analysis with regression analysis. The calculation of classical principal component analysis is based on the regular covariance matrix. The covariance matrix is optimal if the data originated from a multivariate normal distribution, but is very sensitive to the presence of outliers. Alternatives are used to overcome this problem the method of Least Median Square-Minimum Covariance Determinant (LMS-MCD). The purpose of this research is to conduct a comparison between Principal Component Regression (RKU) and Method of Least Median Square - Minimum Covariance Determinant (LMS-MCD) in dealing with outliers. In this study, Method of Least Median Square - Minimum Covariance Determinant (LMS-MCD) has a bias and mean square error (MSE) is smaller than the parameter RKU. Based on the difference of parameter estimators, still have a test that has a difference of parameter estimators method LMS-MCD greater than RKU method.

Author Biographies

I PUTU EKA IRAWAN, Faculty of Mathematics and Natural Sciences, Udayana University
Mathematics Department, Faculty of Mathematics and Natural Sciences, Udayana University
I KOMANG GDE SUKARSA, Faculty of Mathematics and Natural Sciences, Udayana University
Mathematics Department, Faculty of Mathematics and Natural Sciences, Udayana University
NI MADE ASIH, Faculty of Mathematics and Natural Sciences, Udayana University
Mathematics Department, Faculty of Mathematics and Natural Sciences, Udayana University
Published
2013-11-29
How to Cite
IRAWAN, I PUTU EKA; SUKARSA, I KOMANG GDE; ASIH, NI MADE. PENERAPAN METODE LEAST MEDIAN SQUARE-MINIMUM COVARIANCE DETERMINANT (LMS-MCD) DALAM REGRESI KOMPONEN UTAMA. E-Jurnal Matematika, [S.l.], v. 2, n. 4, p. 6-10, nov. 2013. ISSN 2303-1751. Available at: <https://ojs.unud.ac.id/index.php/mtk/article/view/7821>. Date accessed: 04 july 2022. doi: https://doi.org/10.24843/MTK.2013.v02.i04.p051.
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Keywords

Multicollinearity; Outlier; Principal Component Regression; LMS; MCD