Metode Penanganan Multikolinieritas pada RLB: Perbandingan Partial Least Square dengan Ridge Regression
DOI:
https://doi.org/10.34123/jurnalasks.v8i2.55Keywords:
RLB, OLS, Multikolinieritas, RR, PLSRAbstract
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.
Downloads
Download data is not yet available.
References
Abdi, H. 2007. Partial Least Square Regression (PLS-Regression). Ensyclopedia of Measurement and Statistics, 1-13. http://plstools.googlecode.com/svn-history/r13/trunk/Documentation/Abdi-PLSR2007-pretty.pdf (Diakses 21 Maret, 2015).
Adnan, N., Ahmad, M. H., dan Adnan, R. 2006. A Comparative Study On Some Methods For Handling Multicolinierity Problems. Journal of Matematics, Vol. 22 No. 2, 109-119. http://www.matematika.utm.my/index.php/matematika/article/viewFile/179/174. (Diakses 19 Maret, 2015).
Garthwaite, P. H. 1994. An Intepretation of Partial Least Squares. Journal of the American Statistical Association, Vol. 89 No. 425, 122-127. http://amstat.tandfonline.com/doi/abs/10.1080/01621459.1994.10476452 . (Diakses, 20 Maret 2015).
Kibria, B. M. 2003. Performance of Some New Ridge Regression Estimators. Communications in Statistics-Simulation and Computation, Vol. 32 No. 2, 419-435. http://www.tandfonline.com/doi/abs/10.1081/SAC-120017499. (Diakses, 24 Maret 2015).
Netter, J., Wasserman, W., dan H.Kutner, M. 1989. Applied Linier Regression Model (2th ed). Homewood: IRWIN Book Team.
Gujarati, D. N. 2010. Dasar-Dasar Ekonometrika . Jakarta: Salemba Empat.
Soemartini. 2008. Penyelesaian Multikolinieritas Melalui Metode Ridge Regression [Skripsi]. Jatinangor: Universitas Padjadjaran.
Adnan, N., Ahmad, M. H., dan Adnan, R. 2006. A Comparative Study On Some Methods For Handling Multicolinierity Problems. Journal of Matematics, Vol. 22 No. 2, 109-119. http://www.matematika.utm.my/index.php/matematika/article/viewFile/179/174. (Diakses 19 Maret, 2015).
Garthwaite, P. H. 1994. An Intepretation of Partial Least Squares. Journal of the American Statistical Association, Vol. 89 No. 425, 122-127. http://amstat.tandfonline.com/doi/abs/10.1080/01621459.1994.10476452 . (Diakses, 20 Maret 2015).
Kibria, B. M. 2003. Performance of Some New Ridge Regression Estimators. Communications in Statistics-Simulation and Computation, Vol. 32 No. 2, 419-435. http://www.tandfonline.com/doi/abs/10.1081/SAC-120017499. (Diakses, 24 Maret 2015).
Netter, J., Wasserman, W., dan H.Kutner, M. 1989. Applied Linier Regression Model (2th ed). Homewood: IRWIN Book Team.
Gujarati, D. N. 2010. Dasar-Dasar Ekonometrika . Jakarta: Salemba Empat.
Soemartini. 2008. Penyelesaian Multikolinieritas Melalui Metode Ridge Regression [Skripsi]. Jatinangor: Universitas Padjadjaran.
Downloads
Published
2016-12-31
How to Cite
Putri, Y. A., & Anggorowati, M. A. (2016). Metode Penanganan Multikolinieritas pada RLB: Perbandingan Partial Least Square dengan Ridge Regression. Jurnal Aplikasi Statistika & Komputasi Statistik, 8(2), 10. https://doi.org/10.34123/jurnalasks.v8i2.55
Issue
Section
Articles
