PENERAPAN METODE BOOTSTRAP RESIDUAL DALAM MENGATASI BIAS PADA PENDUGA PARAMETER ANALISIS REGRESI

  • NI MADE METTA ASTARI Faculty of Mathematics and Natural Sciences, Udayana University
  • NI LUH PUTU SUCIPTAWATI Faculty of Mathematics and Natural Sciences, Udayana University
  • I KOMANG GDE SUKARSA Faculty of Mathematics and Natural Sciences, Udayana University

Abstract

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.

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Author Biographies

NI MADE METTA ASTARI, Faculty of Mathematics and Natural Sciences, Udayana University
Mathematics Department, Faculty of Mathematics and Natural Sciences, Udayana University
NI LUH PUTU SUCIPTAWATI, Faculty of Mathematics and Natural Sciences, Udayana University
Mathematics Department, Faculty of Mathematics and Natural Sciences, Udayana University
I KOMANG GDE SUKARSA, Faculty of Mathematics and Natural Sciences, Udayana University
Mathematics Department, Faculty of Mathematics and Natural Sciences, Udayana University
Published
2014-11-28
How to Cite
ASTARI, NI MADE METTA; SUCIPTAWATI, NI LUH PUTU; SUKARSA, I KOMANG GDE. PENERAPAN METODE BOOTSTRAP RESIDUAL DALAM MENGATASI BIAS PADA PENDUGA PARAMETER ANALISIS REGRESI. E-Jurnal Matematika, [S.l.], v. 3, n. 4, p. 130 - 137, nov. 2014. ISSN 2303-1751. Available at: <https://ojs.unud.ac.id/index.php/mtk/article/view/11994>. Date accessed: 21 nov. 2024. doi: https://doi.org/10.24843/MTK.2014.v03.i04.p075.
Section
Articles

Keywords

regression analysis; outlier; biases; bootstrap residuals

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