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.
Highlights
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
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
Summary
Walpole et al (2011) menyatakan bahwa analisis regresi merupakan suatu metode statistika yang mempelajari tentang pola hubungan secara sistematis antara variabel dependen ( ) dengan satu atau lebih variabel independen ( ). Dalam analisis regresi biasanya metode yang digunakan untuk melakukan estimasi parameter adalah Metode Kuadrat Terkecil. Untuk mengatasi keterbatasan dari metode kuadrat terkecil berkembanglah metode regresi kuantil untuk estimasi parameter regresi linear. Menurut Uthami.dkk (2013), regresi kuantil pertama kali diperkenalkan oleh Roger Koenker dan Gilbert Basset 1978, regresi ini bertujuan untuk memperluas ide-ide dalam estimasi model fungsi kuantil bersyarat, dimana distribusi kuantil bersyarat dari variabel dependen dinyatakan sebagai fungsi dari kovariat yang diamati. Berdasarkan uraian di atas, maka akan dilakukan penelitian tentang estimasi parameter regresi linear menggunakan regresi kuantil. Penelitian ini mengkaji secara teori regresi kuantil dalam mengatasi pelanggaran asumsi pada metdoe kuadrat terkecil
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