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Kemampuan Estimator Spline Linear dalam Analisis Komponen Utama

ESTIMASI: Journal of Statistics and Its Application · Vol. 1 No. 1 (2022) · pp. 40–47

DOI
10.20956/ejsa.v1i1.9262
ISSN
2721-379X, 2721-3803
Published
2022-02-02
Publisher
Hasanuddin University
Language
en

Abstract

In the formation of a regression model there is a possibility of a relationship between one predictor variable with other predictor variables known as multicollinearity. In the parametric approach, multicollinearity can be overcome by the principal component analysis method. Principal component analysis (PCA) is a multivariate analysis that transforms the originating variables that are correlated into new variables that are not correlated by reducing a number of these variables so that they have smaller dimensions but can account for most of the diversity of the original variables. In some research data that do not form parametric patterns also allows the occurrence of multicollinearity on the predictor variables. This study examines the ability of spline estimators in the analysis of the main components. The data contained multicollinearity and was applied to diabetes mellitus data by taking cholesterol type factors as predictors. Based on the estimation results, one main component is obtained to explain the diversity of variables in diabetes data with the best linear spline model at one knot point.

Keywords

Collaboration network of the authors

40 authors · 105 co-authorship links · showing the 40 most connected

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Sultan Ageng Tirtayasa University
  • Institut Agama Islam Negeri Kendari
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Nirwan IlyasAffiliation unknown4
Anisa AnisaSultan Ageng Tirtayasa University2
Sri Astuti ThamrinAffiliation unknown2
Anisa KalondengAffiliation unknown2
A.Muthiah Nur AngrianyAffiliation unknown2
Zhazha Alifkhamulki RamdhaniAffiliation unknown2
Nurtiti SunusiAffiliation unknown2
Dewi Rahma EnteAffiliation unknown2
Andi Kresna JayaAffiliation unknown2
Muhammad FadilAffiliation unknown2
Widya Nauli Amalia PuteriAffiliation unknown1
Bunga ApriliaAffiliation unknown1
Andi Sitti Fahmi Riyanti HufainiAffiliation unknown1
Dwicahyo Ramadhan PriyatnaAffiliation unknown1
La Podje TalangkoAffiliation unknown1
Vieri KoerniawanAffiliation unknown1
Andi Tenri Riski AmaliaAffiliation unknown1
Muhammad Jayzul UsrahAffiliation unknown1
AnisaAffiliation unknown1
Novilia JaoAffiliation unknown1
Ainun Utari BudistiharahAffiliation unknown1
Mamik UtamiAffiliation unknown1
Sitti SahrimanAffiliation unknown1
Fitri AnnisaAffiliation unknown1
Fitri FitriAffiliation unknown1
Siswanto SiswantoAffiliation unknown1
Hajratul Ashwad KAffiliation unknown1
Andi Ummi Melin Aicha AichaAffiliation unknown1
Asnidar AsnidarAffiliation unknown1
Nurul HikmahInstitut Agama Islam Negeri Kendari1
Nasrah SirajangAffiliation unknown1
Fauziah NurhidayahAffiliation unknown1
Anni Ivoni ParapaAffiliation unknown1
Andi M Alfin BasoAffiliation unknown1
A. Elisha WulandariAffiliation unknown1
Belia Nurul FajriAffiliation unknown1
Abdullah PannuAffiliation 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">Kemampuan Estimator Spline Linear dalam Analisis Komponen Utama</dc:title>
	<dc:creator xml:lang="en">Arifin, Samsul</dc:creator>
	<dc:creator xml:lang="en">Islamiyati, Anna</dc:creator>
	<dc:creator xml:lang="en">Raupong, Raupong</dc:creator>
	<dc:subject xml:lang="en">Principal Component Analysis</dc:subject>
	<dc:subject xml:lang="en">Diabetes Data</dc:subject>
	<dc:subject xml:lang="en">Linear</dc:subject>
	<dc:subject xml:lang="en">Multicollinearity</dc:subject>
	<dc:subject xml:lang="en">Spline.</dc:subject>
	<dc:description xml:lang="en">In the formation of a regression model there is a possibility of a relationship between one predictor variable with other predictor variables known as multicollinearity. In the parametric approach, multicollinearity can be overcome by the principal component analysis method. Principal component analysis (PCA) is a multivariate analysis that transforms the originating variables that are correlated into new variables that are not correlated by reducing a number of these variables so that they have smaller dimensions but can account for most of the diversity of the original variables. In some research data that do not form parametric patterns also allows the occurrence of multicollinearity on the predictor variables. This study examines the ability of spline estimators in the analysis of the main components. The data contained multicollinearity and was applied to diabetes mellitus data by taking cholesterol type factors as predictors. Based on the estimation results, one main component is obtained to explain the diversity of variables in diabetes data with the best linear spline model at one knot point.</dc:description>
	<dc:publisher xml:lang="en">Hasanuddin University</dc:publisher>
	<dc:date>2022-02-02</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/9262</dc:identifier>
	<dc:identifier>10.20956/ejsa.v1i1.9262</dc:identifier>
	<dc:source xml:lang="en">ESTIMASI: Journal of Statistics and Its Application; Vol. 1, No. 1, Januari, 2020 : Estimasi; 40-47</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/9262/6805</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2020 ESTIMASI: Journal of Statistics and Its Application</dc:rights>
</oai_dc:dc>