Authors:

I PUTU EKA IRAWAN, I KOMANG GDE SUKARSA, NI MADE ASIH

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.”

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PDF:

https://jurnal.harianregional.com/mtk/full-7821

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://jurnal.harianregional.com/mtk/id-7821. Date accessed: 28 Aug. 2025. doi:https://doi.org/10.24843/MTK.2013.v02.i04.p051.

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Issue

Vol 2 No 4 (2013)

Section

Articles

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