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EFEKTIVITAS REGRESI KUANTIL DALAM MENGATASI PONTENSIAL PENCILAN

BAREKENG: Jurnal Ilmu Matematika dan Terapan · Vol. 14 No. 2 (2020) · hlm. 301–308

DOI
10.30598/barekengvol14iss2pp301-308
ISSN
2615-3017, 1978-7227
Terbit
2020-06-01
Penerbit
PATTIMURA UNIVERSITY
Bahasa
eng

Abstrak

Quantile regression as a robust regression method can be used to overcome the impact of unusual cases on regression estimates such as the presence of potential outliers in the data. The purpose of this study was to evaluate the effectiveness of quantile regression in dealing with potential outliers in multiple linear regression compared to ordinary least square (OLS). This study used simulation data in multiple regression model with the number of independent variables (p=3) for different sample sizes (n = 20, 40, 60, 100, 200) and and repeated 1000 times. The effectiveness of the quantile regression method and OLS in estimating β parameters was measured by Mean square error (MSE) and the best model is chosen based on the smallest Akaike Information Criterion (AIC) value. The results showed that in contrast to OLS, quantile regression was able to deal with potential outliers and provided a better estimator with a smaller mean mean square error. Compared to OLS and other quantiles, this study also provides sufficient results that quantile 0.5 provides the best parameter estimate and the best model based on the smallest MSE and AIC values.

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Sumber metadata: OAI-PMH (metadata jurnal) https://ojs3.unpatti.ac.id/index.php/barekeng/oai, pertama di-harvest 5 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en-US">EFEKTIVITAS REGRESI KUANTIL DALAM MENGATASI PONTENSIAL PENCILAN</dc:title>
	<dc:creator>Herawati, Netti</dc:creator>
	<dc:description xml:lang="en-US">Quantile regression as a robust regression method can be used to overcome the impact of unusual cases on regression estimates such as the presence of potential outliers in the data. The purpose of this study was to evaluate the effectiveness of quantile regression in dealing with potential outliers in multiple linear regression compared to ordinary least square (OLS). This study used simulation data in multiple regression model with the number of independent variables (p=3) for different sample sizes (n = 20, 40, 60, 100, 200) and&amp;nbsp;  &amp;nbsp;and  &amp;nbsp;repeated 1000 times. The effectiveness of the quantile regression method and OLS in estimating β&amp;nbsp;&amp;nbsp; parameters was measured by Mean square error (MSE) and the best model is chosen based on the smallest Akaike Information Criterion (AIC) value. The results showed that in contrast to OLS, quantile regression was able to deal with potential outliers and provided a better estimator with a smaller mean mean square error. Compared to OLS and other quantiles, this study also provides sufficient results that quantile 0.5 provides the best parameter estimate and the best model based on the smallest MSE and AIC values.</dc:description>
	<dc:publisher xml:lang="en-US">PATTIMURA UNIVERSITY</dc:publisher>
	<dc:date>2020-06-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/2004</dc:identifier>
	<dc:identifier>10.30598/barekengvol14iss2pp301-308</dc:identifier>
	<dc:source xml:lang="en-US">BAREKENG: Jurnal Ilmu Matematika dan Terapan; Vol 14 No 2 (2020): BAREKENG: Jurnal Ilmu Matematika dan Terapan; 301-308</dc:source>
	<dc:source>2615-3017</dc:source>
	<dc:source>1978-7227</dc:source>
	<dc:source>10.30598/barekengvol14iss2year2020</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/2004/2132</dc:relation>
	<dc:coverage xml:lang="en-US">simulation</dc:coverage>
</oai_dc:dc>