DOI: https://doi.org/10.14710/j.gauss.12.3.330-339

KAJIAN SIMULASI PERBANDINGAN METODE RIDGE REGRESSION DAN ADJUSTED RIDGE REGRESSION UNTUK PENANGANAN MULTIKOLINEARITAS

*Choirun Nisa  -  Program Studi Ilmu Komunikasi, Fakultas Ekonomi dan Sosial, Universitas Amikom Yogyakarta, Indonesia
Siti Hariati Hastuti  -  , Indonesia
Open Access Copyright 2023 Jurnal Gaussian under http://creativecommons.org/licenses/by-nc-sa/4.0.

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Abstract
Regression analysis is widely used in research. However, often in using this analysis the assumption of non-multicollinearity is not fulfilled. Handling of these problems can be done using Ridge Regression (RR) and Adjusted Ridge Regression (AR) methods. This study aims to compare the performance of RR and AR in handling multicollinearity among explanatory variables in multiple regression analysis using data simulation. The simulated data contain various multicollinearity level (ρ = 0.6, 0.8, 0.9) with of each different sample size (n = 20, 50, 100). The performance of the two methods are compared using Mean Square Errors (MSE). The result shows that the AR method and the RR method produce a smaller MSE value when the sample size used is larger. The MSE value generated by the AR method tends to be smaller than the RR method which can be seen from each data repetition used. It shows that the AR method is relatively more effective than the RR method for dealing with multicollinearity problems.
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Keywords: multicollinearity; ridge regression; adjusted ridge regression; MSE.

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  1. Daoud, J.I., 2017. Multicollinearity and Regression Analysis. J. Phys. Conf. Ser. 949, 012009. https://doi.org/10.1088/1742-6596/949/1/012009
  2. Dorugade, A.V., 2016. Adjusted ridge estimator and comparison with Kibria’s method in linear regression. J. Assoc. Arab Univ. Basic Appl. Sci. 21, 96–102. https://doi.org/10.1016/j.jaubas.2015.04.002
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  4. Hoerl, A.E., Kennard, R.W., 1970. Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics 12, 55–67. https://doi.org/10.1080/00401706.1970.10488634
  5. Iqbal, M.A., 2021. Application of Regression Techniques with their Advantages and Disadvantages 4, 11–17
  6. Iriawan, N., Astuti, S.P., 2006. Mengolah data statistik dengan mudah menggunakan minitab 14. Yogyak. Andi
  7. Montgomery, D.C., Peck, E.A., 1992. Introduction to Linear Regression Analysis, Wiley Series in Probability and Statistics - Applied Probability and Statistics Section. Wiley
  8. Rodgers, J.L., Nicewander, W.A., 1988. Thirteen Ways to Look at the Correlation Coefficient. Am. Stat. 42, 59–66. https://doi.org/10.2307/2685263
  9. Sulistianingsih, E., Suparti, S., Ispriyanti, D., 2023. Pemodelan Indeks Pembangunan Manusia di Jawa Tengah Menggunakan Metode Regresi Ridge dan Regresi Stepwise. J. Gaussian 11, 468–477. https://doi.org/10.14710/j.gauss.11.3.468-477
  10. Tazliqoh, A.Z., Rahmawati, R., Safitri, D., 2015. Perbandingan Regresi Komponen Utama dengan Regresi Ridge pada Analisis Faktor-Faktor Pendapatan Asli Daerah (PAD) Provinsi Jawa Tengah. J. Gaussian 4, 1–10. https://doi.org/10.14710/j.gauss.4.1.1-10

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