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Yulia Atma Putri
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Computer Science & IT Decision Sciences, Operations Research & Management Mathematics

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Metode Penanganan Multikolinieritas pada RLB: Perbandingan Partial Least Square dengan Ridge Regression Yulia Atma Putri; Margaretha Ari Anggorowati
Jurnal Aplikasi Statistika & Komputasi Statistik Vol 8 No 2 (2016): Journal of Statistical Application and Computational Statistics
Publisher : Pusat Penelitian dan Pengabdian Masyarakat Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (730.155 KB) | DOI: 10.34123/jurnalasks.v8i2.55

Abstract

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.
Co-Authors Margaretha Ari Anggorowati