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IN
Journal article
PENERAPAN METODE BOOTSTRAP RESIDUAL DALAM MENGATASI BIAS PADA PENDUGA PARAMETER ANALISIS REGRESI
Ni Made Metta Astari Ni Luh Putu Suciptawati I KOMANG GDE SUKARSA
Volume : 3 Nomor : 4 Published : 2014, November
E-JURNAL MATEMATIKA - Jurusan Matematika, Fakultas MIPA Universitas Udayana
Abstrak
Statistical analysis which aims to analyze a linear relationship between the independent variable and the dependent variable is known as regression analysis. To estimate parameters in a regression analysis method commonly used is the Ordinary Least Square (OLS). But the assumption is often violated in the OLS, the assumption of normality due to one outlier. As a result of the presence of outliers is parameter estimators produced by the OLS will be biased. Bootstrap Residual is a bootstrap method that is applied to the residual resampling process. The results showed that the residual bootstrap method is only able to overcome the bias on the number of outliers 5% with 99% confidence intervals. The resulting parameters estimators approach the residual bootstrap values OLS initial allegations were also able to show that the bootstrap is an accurate prediction tool. Keywords: regression analysis, outlier, biases, bootstrap residuals