Metode Penanganan Multikolinieritas pada RLB: Perbandingan Partial Least Square dengan Ridge Regression


Yulia Atma Putri Margaretha Ari Anggorowati


Multicolinierity between variable predictor in multiple regression is assuming violation  for ordinary least square estimator (OLS). Ridge Regression (RR) and Partial Least Square Regression (PLSR were used to handle the multicolinierity problem. RR modify OLS by adding subjective bias consatant, while PLSR, generalize and combine Principal Component Analisis and multiple regression. The efficiency of these two methods will be compared based on the value of RMSE. This study simulated generating data in different level of multicolinearity, the number of variabel, and number of observation were controlled. This study results that, overall, both method equally efficient.


How to Cite
PUTRI, Yulia Atma; ANGGOROWATI, Margaretha Ari. Metode Penanganan Multikolinieritas pada RLB: Perbandingan Partial Least Square dengan Ridge Regression. Jurnal Aplikasi Statistika & Komputasi Statistik, [S.l.], v. 8, n. 2, p. 10, dec. 2016. ISSN 2615-1367. Available at: <>. Date accessed: 02 aug. 2021. doi:


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