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All Journal Desimal: Jurnal Matematika BAREKENG: Jurnal Ilmu Matematika dan Terapan Buguh: Jurnal Pengabdian Kepada Masyarakat International Journal of Electronics and Communications Systems Jurnal Siger Matematika
Saidi, Subian
Jurusan Matematika, FMIPA, Universitas Lampung
Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Physics Social Sciences Transportation

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RIDGE LEAST ABSOLUTE DEVIATION PERFORMANCE IN ADDRESSING MULTICOLLINEARITY AND DIFFERENT LEVELS OF OUTLIER SIMULTANEOUSLY Herawati, Netti; Saidi, Subian; Azis, Dorrah
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 16 No 3 (2022): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (481.598 KB) | DOI: 10.30598/barekengvol16iss3pp779-786

Abstract

If there is multicollinearity and outliers in the data, the inference about parameter estimation in the LS method will deviate due to the inefficiency of this method in estimating. To overcome these two problems simultaneously, it can be done using robust regression, one of which is ridge least absolute deviation method. This study aims to evaluate the performance of the ridge least absolute deviation method in surmounting multicollinearity in divers sample sizes and percentage of outliers using simulation data. The Monte Carlo study was designed in a multiple regression model with multicollinearity (ρ=0.99) between variables and and outliers 10%, 20%, 30% on response variables with different sample sizes (n = 25, 50,75,100,200; =0, and β=1 otherwise). The existence of multicollinearity in the data is done by calculating the correlation value between the independent variables and the VIF value. Outlier detection is done by using boxplot. Parameter estimation was carried out using the RLAD and LS methods. Furthermore, a comparison of the MSE values of the two methods is carried out to see which method is better in overcoming multicollinearity and outliers. The results showed that RLAD had a lower MSE than LS. This signifies that RLAD is more precise in estimating the regression coefficients for each sample size and various outlier levels studied.
Co-Authors Aang Nuryaman Ahmad Faisol Anisa Mulyani Ayu Febriani Azis, Dorrah Azzahra Zatil Irfani Dea Kusniar Dorrah Azis Eri Setiawan Eri Setiawan Eri Setiawan Eri Setiawan Febi Eka Febriansyah Ferdinandus Ivanelian Jodie Tori Khoirin Nisa Khoirin Nisa Khoirin Nisa La Zakaria Mariska, Yomi Muhamad Yoga Muhammad Luthfi Muhammad Ridho Pratama Muharramah, Ulfah Netti Herawati Netti Herawati Netti Herawati Netti Herawati Netti Herawati Nusyirwan Nusyirwan Pratama, Rinaldo Adi Ruby, Triyono Rudi Ruswandi Sofalina Nodra Brilliantya Sutrisno, Agus Tiryono Ruby Tiryono Ruby Triyono Ruby Ulfah Muharramah Yomi Mariska Yusuf Perdana Zakaria, La Zatti Alikum