Perbandingan Regresi LASSO dan Principal Component Regression dalam Mengatasi Masalah Multikolinearitas

Elsa Fadillah Nasution, Rahmawati Pane

Abstract


Multicollinearity is a problem that often arises in regression analysis. If the assumption of the absence of multicollinearity is not met, researchers will have difficulty in identifying independent variables that have a significant effect in the regression model. The presence of multicollinearity can cause the estimation of regression parameters in the Ordinary Least Square (OLS) method to be inefficient. To overcome this, the LASSO Regression method and the Principal Component Regression (PCR) method are used. The data used in this study are generation data derived from low (0,1-0,3), medium (0,4-0,6), and high (0,7-0,9) correlation levels with different sample sizes (n=20,40,120,200) from normal distribution with 30 and 60 independent variables. The performance of LASSO Regression method and Principal Component Regression (PCR) method is evaluated using Mean Square Error (MSE) value and coefficient of determination ( ) value. Based on this research, the LASSO Regression method has better efficiency than the Principal Component Regression (PCR) method because the LASSO Regression method obtains a smaller Mean Square Error (MSE) value and a higher coefficient of determination ( ) value than the Principal Component Regression (PCR) method.


Keywords


Multicollinearity, Principal Component Regression (PCR), LASSO Regression

References


Boediono. (2001). Statistik dan Probabilitas. PT Remaja Rosdakarya.

Delsen, M. S. N. Van, Wattimena, A. Z., & Saputri, S. D. (2017). Penggunaan Metode Analisis Komponen Utama untuk Mereduksi Faktor-Faktor Inflasi di Kota Ambon. Jurnal Ilmu Matematika Dan Terapan, 11(2), 109–118.

Draper, N. R., & Smith, H. (1992). Applied Regression Analysis (Second). John Wiley.

Gujarati, D. N., & Porter, D. C. (2008). Basic Economterics (Fourth). McGraw-Hill.

Irwan, M. (2015). Least Square and Ridge Regression Estimation. Jurnal MSA, 3(2), 7–13.

Johnson, R. A., & Wichern, D. W. (2007). Applied Multivariate Statistical Analysis (Sixth). Prentice Hall.

Küçükakçah, Z., & Gözükara Bağ, H. (2022). Comparison of Ordinary Least Squares and Principal Components Regression Analyses. The Journal Of Cognitive Systems, 7(22), 7–11. https://doi.org/10.52876/jcs.1117961

Mait, Y. A., Salaki, D. T., & Komalig, H. A. H. (2021). Kajian Model Prediksi Metode Least Absolute Shrinkage and Selection Operator (LASSO) pada Data Mengandung Multikolinearitas. Jurnal Matematika Dan Aplikasi, 10(2), 69–75. https://doi.org/10.35799/dc.10.2.2021.34909

Montgomery, D. C., Peck, E. A., & Vining, G. G. (2012). Introduction to Linear Regression Analysis (Fifth). John Wiley & Sons.

Paul, R. K. (2006). Multicollinearity: Causes, Effects and Remedies. IASRI, 1(1), 58–65.

Pendi. (2021). Analisis Regresi dengan Metode Komponen Utama dalam Mengatasi Masalah Multikolinearitas. Buletin Ilmiah Math. Stat. Dan Terapannya (Bimaster), 10(1), 131–138. http://dx.doi.org/10.26418/bbimst.v10i1.44750

Robbani, M., Agustiani, F., & Herrhyanto, N. (2019). Regresi Least Absolute Shrinkage and Selection Operator (LASSO) pada Kasus Inflasi di Indonesia Tahun 2014-2017. Jurnal Eurekamatika, 7(2). https://doi.org/10.17509/jem.v7i2.22130

Sartika, I., Debataraja, N. N., & Imro’ah, N. (2020). Analisis Regresi dengan Metode Least Absolute Shrinkage and Selection Operator (LASSO) dalam Mengatasi Multikolinearitas. Buletin Ilmiah Math. Stat. Dan Terapannya, 9(1), 31–38. https://dx.doi.org/10.26418/bbimst.v9i1.38029

Sarwoko. (2005). Dasar-Dasar Ekonomterika. ANDI.

Tibshirani, R. (1996). Regression Shinkrage and Selection Via The LASSO. Journal of the Royal Statistical Society Series B (Methodological), 58(1), 267–288. https://www.jstor.org/stable/2346178




DOI: https://doi.org/10.30743/mes.v10i1.9656

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