BibTex Citation Data :
@article{J.Gauss26670, author = {Tsania Faizia and Alan Prahutama and Hasbi Yasin}, title = {PEMODELAN INDEKS PEMBANGUNAN MANUSIA DI JAWA TENGAH DENGAN REGRESI KOMPONEN UTAMA ROBUST}, journal = {Jurnal Gaussian}, volume = {8}, number = {2}, year = {2019}, keywords = {Robust Principal Component Regression, Multicollinearity, Outliers, Minimum Covariance Determinant (MCD), Least Trimmed Squares (LTS), Human Development Index (HDI).}, abstract = { Robust principal component regression is development of principal component regression that applies robust method at principal component analysis and principal component regression analysis. Robust principal component regression does not only overcome multicollinearity problems, but also overcomes outlier problems. The robust methods used in this research are Minimum Covariance Determinant (MCD) that is applied when doing principal component analysis and Least Trimmed Squares (LTS) that is applied when doing principal component regression analysis. The case study in this research is Human Development Index (HDI) in Central Java in 2017 which is influenced by labor force participation rates, school enrollment rates, percentage of poor population, population aged 15 years and over who are employed, health facilities, gross enrollment rates, and net enrollment rates. The model of HDI in Central Java in 2017 using robust principal component regression MCD-LTS provides the most effective result for handling multicollinearity and outliers with Adjusted R 2 value of 0.6913 and RSE value of 0.469. Keywords: Robust Principal Component Regression, Multicollinearity, Outliers, Minimum Covariance Determinant (MCD), Least Trimmed Squares (LTS), Human Development Index (HDI).}, issn = {2339-2541}, pages = {253--271} doi = {10.14710/j.gauss.8.2.253-271}, url = {https://ejournal3.undip.ac.id/index.php/gaussian/article/view/26670} }
Refworks Citation Data :
Robust principal component regression is development of principal component regression that applies robust method at principal component analysis and principal component regression analysis. Robust principal component regression does not only overcome multicollinearity problems, but also overcomes outlier problems. The robust methods used in this research are Minimum Covariance Determinant (MCD) that is applied when doing principal component analysis and Least Trimmed Squares (LTS) that is applied when doing principal component regression analysis. The case study in this research is Human Development Index (HDI) in Central Java in 2017 which is influenced by labor force participation rates, school enrollment rates, percentage of poor population, population aged 15 years and over who are employed, health facilities, gross enrollment rates, and net enrollment rates. The model of HDI in Central Java in 2017 using robust principal component regression MCD-LTS provides the most effective result for handling multicollinearity and outliers with Adjusted R2 value of 0.6913 and RSE value of 0.469.
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