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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 · 97 co-authorship links · showing the 40 most connected

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  • Sultan Ageng Tirtayasa University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
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Co-authorOrganisationJoint articles
Erna Tri HerdianiAffiliation unknown6
Nasrah SirajangAffiliation unknown3
Andi Kresna JayaAffiliation unknown3
RaupongAffiliation unknown2
La Podje TalangkoAffiliation unknown2
Andi Isna YunitaAffiliation unknown2
Iis Cendrah KasihAffiliation unknown1
Rizki AdiputraAffiliation unknown1
Risma RismaAffiliation unknown1
Ika FitrianaAffiliation unknown1
Dewi Santika Upa P.Affiliation unknown1
Asti Inayati MagfirahAffiliation unknown1
Mega Selvia TjahayaAffiliation unknown1
Ririn AriantiAffiliation unknown1
Muhammad IdmanAffiliation unknown1
Raupong RaupongAffiliation unknown1
Fitri AnnisaAffiliation unknown1
Nur Alfianingsih RajaAffiliation unknown1
Aqilah Salsabila RahmanAffiliation unknown1
Mukrimin AdamAffiliation unknown1
Anisa Haura Salsa Fatih YusufAffiliation unknown1
Nurhidaya LAffiliation unknown1
Anisa AnisaSultan Ageng Tirtayasa University1
Victor LimanAffiliation unknown1
Sonya SonyaAffiliation unknown1
Rael Hofni TandirerungAffiliation unknown1
Anisa KalondengAffiliation unknown1
Surya Cahyadi SyamAffiliation unknown1
Nirwan NirwanAffiliation unknown1
Nurhidayatullah NurhidayatullahAffiliation unknown1
Amalia AndrianingrumAffiliation unknown1
Siswanto SiswantoAffiliation unknown1
A.Muthiah Nur AngrianyAffiliation unknown1
Rini JamalAffiliation unknown1
Andi M Alfin BasoAffiliation unknown1
Andi FebriyantiAffiliation unknown1
Rahmiati RahimAffiliation unknown1

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>