Estimasi Parameter Regresi Ridge Robust pada Data Profil Kesehatan Sulawesi Selatan
Keywords:
Robust Ridge Regression, Least Trimmed Square, Multicollinearity, Outlier, Infant Mortality RateAbstract
ABSTRACT
Multicollinearity is one of the assumption violations in regression analysis. The existence of multicollinearity causes the standard error to increase. Ridge regression is one of the regression analysis approaches that can overcome this problem. Besides multicollinearity, another problem that often occurs is outliers. The existence of outliers causes the data not to be normally distributed. Ridge Robust Least Trimmed Square Regression is a method that can be used to overcome multicollinearity and outlier problems in the data simultaneously in the regression analysis model. The purpose of this study was to obtain the estimation results of the least trimmed square ridge robust regression model on the Health Profile data of South Sulawesi in 2017. From the results and discussion it was found that the least trimmed square ridge robust regression method has an Rsquare value or ?2 which is 88% and an MSE value 1.96, thus indicating that the ridge robust least trimmed square model fits better in dealing with data containing multicollinearity and outliers.
Keywords: Robust Ridge Regression, Least Trimmed Square, Multicollinearity, Outlier, Infant Mortality Rate.
References
Supranto, J. Ekonometri. Bogor: Ghalia Indonesia. 2005.
Cahyawati, D. Efektifitas Metode Regresi Robust Penduga Welsch dalam Mengatasi Pencilan pada Pemodelan Regresi Linear Berganda. Jurnal Penelitian Sains, 12(1). Unsri, 2009.
Montgomery, D. C., & Peck, E. A. Introduction to Linear Regression Analysis (2nd ed). New York: John Wiley & SonsInc. 1992.
Jarque, C. M., & Bera, A. K. A Test for Normality of Observations and Regression Residuals. International Statistical Review, 163-172, 1987.
Soemartini. Pencilan (Outlier). Universitas Padjadjaran, Bandung melalui http://resources.unpad.ac.id/unpad-content/uploads/publikasidosen/. Outlier(Pencilan).pdf. 2007.
Chen, C. Robust Regression and Outlier Detection with the ROBUSTREG Procedure. Cary NC: SAS Institute Inc. 2002.
Rousseuw, P. J. & Leroy, A. M. Robust Regression and Outlier Detection. New York: John Wiley & Sons, Inc. 1987.
Samkar, H. & Alpu, O. Ridge Regression Based on Some Robust Estimators. Journal of Modern Applied Statistical Methods. 9, 45-501, 2010.
Gujarati, D. Ekonometrika Dasar Terjemahan Sumarno Zain. Jakarta:Erlangga. 2003.
Downloads
Published
Issue
Section
License
Copyright
It is the author's responsibility to ensure that his or her submitted work does not infringe any existing copyright. Authors should obtain permission to reproduce or adapt copyrighted material and provide evidence of approval upon submitting the final version of a manuscript.
