PENERAPAN METODE BOOTSTRAP RESIDUAL DALAM MENGATASI BIAS PADA PENDUGA PARAMETER ANALISIS REGRESI
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.Downloads
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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.
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Section
Articles
Keywords
regression analysis; outlier; biases; bootstrap residuals
This work is licensed under a Creative Commons Attribution 4.0 International License.