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Estimasi Parameter Regresi Ridge Robust pada Data Profil Kesehatan Sulawesi Selatan

ESTIMASI: Journal of Statistics and Its Application · Vol. 5 No. 2 (2024) · pp. 169–183

DOI
10.20956/ejsa.v5i2.25520
ISSN
2721-379X, 2721-3803
Published
2024-07-27
Publisher
Hasanuddin University
Language
en

Abstract

ABSTRACT Multicollinearity is one of the assumption violations in regression analysis. The existence of multicollinearity causes the standard error to increase. Ridge regression is one of the regression analysis approaches that can overcome this problem. Besides multicollinearity, another problem that often occurs is outliers. The existence of outliers causes the data not to be normally distributed. Ridge Robust Least Trimmed Square Regression is a method that can be used to overcome multicollinearity and outlier problems in the data simultaneously in the regression analysis model. The purpose of this study was to obtain the estimation results of the least trimmed square ridge robust regression model on the Health Profile data of South Sulawesi in 2017. From the results and discussion it was found that the least trimmed square ridge robust regression method has an Rsquare value or?2 which is 88% and an MSE value 1.96, thus indicating that the ridge robust least trimmed square model fits better in dealing with data containing multicollinearity and outliers. Keywords: Robust Ridge Regression, Least Trimmed Square, Multicollinearity, Outlier, Infant Mortality Rate.

Keywords

Collaboration network of the authors

40 authors · 100 co-authorship links · showing the 40 most connected

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  • Hasanuddin University
  • Affiliation unknown
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  • Affiliation unknown
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Co-authorOrganisationJoint articles
Erna Tri HerdianiHasanuddin University6
M. Zaky Hisyam GozhiHasanuddin University3
Anisa KalondengHasanuddin University3
Nasrah SirajangHasanuddin University2
RaupongAffiliation unknown2
Muh. Ikbal TaufikHasanuddin University2
Nurtiti SunusiHasanuddin University2
Andi Kresna JayaHasanuddin University2
Raupong RaupongHasanuddin University2
Siswanto SiswantoHasanuddin University2
Iis Cendrah KasihHasanuddin University1
Rizki AdiputraHasanuddin University1
Risma RismaAffiliation unknown1
Ika FitrianaHasanuddin University1
Dewi Santika Upa P.Affiliation unknown1
Asti Inayati MagfirahAffiliation unknown1
Mega Selvia TjahayaAffiliation unknown1
Andi Isna YunitaHasanuddin University1
Fitri AnnisaAffiliation unknown1
Nur Alfianingsih RajaHasanuddin University1
Aqilah Salsabila RahmanAffiliation unknown1
Anisa Haura Salsa Fatih YusufAffiliation unknown1
Nurhidaya LAffiliation unknown1
Anisa AnisaAffiliation unknown1
Victor LimanAffiliation unknown1
Sonya SonyaAffiliation unknown1
Surya Cahyadi SyamAffiliation unknown1
Alimatun NajihaHasanuddin University1
Andi Sri YuliantiHasanuddin University1
Taufiq AkbarHasanuddin University1
Eunike Laurine RandaHasanuddin University1
Sitti Aisyah SuriandaHasanuddin University1
Guntur Dwi PutraHasanuddin University1
Anisa AnisaHasanuddin University1
Hanifah LainunAffiliation unknown1
Amran AmranHasanuddin University1
A. Muthiah Nur AngrianyHasanuddin University1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journal.unhas.ac.id/index.php/ESTIMASI/oai, first harvested 2 Nov 2026, last seen 2 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en">Estimasi Parameter Regresi Ridge Robust pada Data Profil Kesehatan Sulawesi Selatan</dc:title>
	<dc:creator xml:lang="en">Waibusi, Hendriete Tiur Marowi</dc:creator>
	<dc:creator xml:lang="en">Tinungki, Georgina Maria</dc:creator>
	<dc:creator xml:lang="en">Sahriman, Sitti</dc:creator>
	<dc:subject xml:lang="en">Robust Ridge Regression</dc:subject>
	<dc:subject xml:lang="en">Least Trimmed Square</dc:subject>
	<dc:subject xml:lang="en">Multicollinearity</dc:subject>
	<dc:subject xml:lang="en">Outlier</dc:subject>
	<dc:subject xml:lang="en">Infant Mortality Rate</dc:subject>
	<dc:description xml:lang="en">ABSTRACT
&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp; Multicollinearity is one of the assumption violations in regression analysis. The existence of multicollinearity causes the standard error to increase. Ridge regression is one of the regression analysis approaches that can overcome this problem. Besides multicollinearity, another problem that often occurs is outliers. The existence of outliers causes the data not to be normally distributed. Ridge Robust Least Trimmed Square Regression is a method that can be used to overcome multicollinearity and outlier problems in the data simultaneously in the regression analysis model. The purpose of this study was to obtain the estimation results of the least trimmed square ridge robust regression model on the Health Profile data of South Sulawesi in 2017. From the results and discussion it was found that the least trimmed square ridge robust regression method has an Rsquare value or ?2 which is 88% and an MSE value 1.96, thus indicating that the ridge robust least trimmed square model fits better in dealing with data containing multicollinearity and outliers.
Keywords: Robust Ridge Regression, Least Trimmed Square, Multicollinearity, Outlier, Infant Mortality Rate.
&amp;nbsp;</dc:description>
	<dc:publisher xml:lang="en">Hasanuddin University</dc:publisher>
	<dc:date>2024-07-27</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://journal.unhas.ac.id/index.php/ESTIMASI/article/view/25520</dc:identifier>
	<dc:identifier>10.20956/ejsa.v5i2.25520</dc:identifier>
	<dc:source xml:lang="en">ESTIMASI: Journal of Statistics and Its Application; Vol. 5, No. 2, Juli, 2024 : Estimasi; 169-183</dc:source>
	<dc:source>2721-379X</dc:source>
	<dc:source>2721-3803</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://journal.unhas.ac.id/index.php/ESTIMASI/article/view/25520/11853</dc:relation>
</oai_dc:dc>