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Articles
Published: 2018-12-29

Estimasi Parameter Regresi Linear Menggunakan Regresi Kuantil

(Least square method Quantile regression method Simplex algorithm).

Abstract

Regression analysis is a statistical analysis method for estimating the relationship between dependent variables (Y) and one or more independent variables (X) . As the purpose of this study is to theoretically examine the quantile regression method in estimating linear regression parameters. In regression analysis usually the method used to estimate parameters is the least square method with assumptions that must be met that normal assumption, homoskedasticity, no autocorrelation and non multicollinearity. Basically the least square method is sensitive to the assumptions of deviations in the data, so that the estimations results will be lees good if the assumptions are not fulfilled. Therefore, to overcome the limitations of the least square method developed a quantile regression method for estimating linear regression parameters. Based on the result of research that has been done shows that the estimation of linear regression parameters using the quantile regression method is obtained by minimazing the absolute number of errors through the simplex algorithm.

References

  1. Algifari, 2000, Analisis Regresi: Teori, Kasus dan Solusi, Edisi kedua, BPFE Yogyakarta, Yogyakarta.
  2. Andani, F.P.P. dan Widodo, E., 2016, Perbandingan Kemampuan Regresi Kuantil Median dan Transformasi Box-Cox dalam Mengatasi Heteroskedastisitas, Konferensi Nasional Penelitian Matematika dan Pembelajarannya (KNPMP I), Universitas Muhammadiyah Surakarta, 12 Maret 2016.
  3. Gujarati, D.N., 2004, Basic Econometric, Mc Graw Hill, New York.
  4. Hapsery, A., 2017, Regresi Kuantil Berbasis Model Rekursif dan Estimasi Sparsity untuk Analisis Publikasi Doses ITS di Scopus, Tesis, Jurusan Statistika Fakultas MIPA Institut Teknologi Sepuluh November Surabaya.
  5. Uthami, I.A.P., Sukarsa, I.K.G., dan Kencana, I.P.E., 2013, Regresi Kuantil Median untuk Mengatasi Heteroskedastisitas pada Analisis Regresi, E-Jurnal Matematika Vol.2, No.1, Januari 2013, 6-13.
  6. Walpole, R.E., Raymond, H.M., Shaton, L.M., dan Keying, Y., 2011, Probability and Statistics for Engineers and Scientist, Ninth Edition, Lifland et al. Bookmakers, New York.
  7. Youlanda, S.R., 2015, Perbandingan Metode Regresi Kuantil Median dengan Metode Weighted Least Square (WLS) untuk Menyelesaikan Heteroskedastisitas pada Analisis regresi, Skripsi, Jurusan Matematika Fakultas MIPA Universitas Jember.

How to Cite

Rachmawati, B. D., & Aini, Q. (2018). Estimasi Parameter Regresi Linear Menggunakan Regresi Kuantil. EIGEN MATHEMATICS JOURNAL, 2(2), 37–42. https://doi.org/10.29303/emj.v2i2.15