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SMALL AREA ESTIMATION OF MEAN YEARS SCHOOL IN KABUPATEN BOGOR USING SEMIPARAMETRIC P-SPLINE

BAREKENG: Jurnal Ilmu Matematika dan Terapan · Vol. 16 No. 4 (2022) · pp. 1541–1550

DOI
10.30598/barekengvol16iss4pp1541-1550
ISSN
2615-3017, 1978-7227
Published
2022-12-15
Publisher
PATTIMURA UNIVERSITY
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

The Fay-Herriot model, generally uses the EBLUP (Empirical Best Linear Unbiased Prediction) method, is less flexible due to the assumption of linearity. The P-Spline semiparametric model is a modification of the Fay-Herriot model which can accommodate the presence of two components, linear and nonlinear predictors. This paper also deals spatial dependence among the random area effects so that a model with spatially autocorrelated errors will be implemented, known as the SEBLUP (Spatial Empirical Best Linear Unbiased Prediction) method. Using data from SUSENAS, PODES, and some publication from BPS, the main objective of this study is to estimate the mean years school at kecamatan level in Kabupaten Bogor using EBLUP, Semiparametric P-Spline approach and SEBLUP method. The results show that based on the RRMSE value, the cubic P-Spline model with three knots predicts the mean years school better than EBLUP. Meanwhile, the addition of spatial effects into the small area estimation has not been able to improve the estimated value of the P-Spline semiparametric approach.

Collaboration network of the authors

40 authors · 118 co-authorship links · 4 across different organisations · showing the 40 most connected

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

  • IPB University
  • Bandung Islamic University
  • Binus University
  • Universitas Halu Oleo
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Kusman SadikAffiliation unknown5
Erfiani ErfianiAffiliation unknown4
Khairil Anwar NotodiputroBandung Islamic University4
Anwar FitriantoAffiliation unknown4
Muhammad Nur AidiAffiliation unknown3
Aji Hamim WigenaAffiliation unknown3
Bagus SartonoIPB University3
Erwan SetiawanAffiliation unknown2
Asysta Amalia PasaribuBinus University2
Budi SusetyoAffiliation unknown2
Yudi Fathul AminAffiliation unknown2
Zein Rizky SantosoAffiliation unknown1
Vera Maya SantiAffiliation unknown1
Andi Harismahyanti A.Affiliation unknown1
Agus Mohamad SolehIPB University1
Maulida Fajrining TyasAffiliation unknown1
Reni AmeliaAffiliation unknown1
Husnun NashirAffiliation unknown1
Khusnia Nurul KhikmahAffiliation unknown1
Nila LestariAffiliation unknown1
Elisa D JuliantiAffiliation unknown1
Irvanal HaqAffiliation unknown1
Efriwati EfriwatiAffiliation unknown1
Leli LatifahAffiliation unknown1
Titin YuniartyAffiliation unknown1
Dwi Agustin Nuriani SirodjAffiliation unknown1
Gusti Arviana RahmanUniversitas Halu Oleo1
Arie PurwantoAffiliation unknown1
Muhammad Aziz SuprayogiAffiliation unknown1
Joao Ferreira Rendes Bean LolyAffiliation unknown1
Muhammad Azis SuprayogiAffiliation unknown1
I Made SumertajayaAffiliation unknown1
Agus Sofian Eka HidayatAffiliation unknown1
Dian KusumaningrumAffiliation unknown1
Siskarossa Ika OktoraAffiliation unknown1
Dariani MatualageAffiliation unknown1
Asysta Amalia PasaribuAffiliation 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://ojs3.unpatti.ac.id/index.php/barekeng/oai, first harvested 5 Nov 2026, last seen 5 Nov 2026.
Original metadata (oai_dc)
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	<dc:title xml:lang="en-US">SMALL AREA ESTIMATION OF MEAN YEARS SCHOOL IN KABUPATEN BOGOR USING SEMIPARAMETRIC P-SPLINE</dc:title>
	<dc:creator>Putri, Christiana Anggraeni</dc:creator>
	<dc:creator>Indahwati, Indahwati</dc:creator>
	<dc:creator>Kurnia, Anang</dc:creator>
	<dc:description xml:lang="en-US">The Fay-Herriot model, generally uses the EBLUP (Empirical Best Linear Unbiased Prediction) method, is less flexible due to the assumption of linearity. The P-Spline semiparametric model is a modification of the Fay-Herriot model which can accommodate the presence of two components, linear and nonlinear predictors. This paper also deals spatial dependence among the random area effects so that a model with spatially autocorrelated errors will be implemented, known as the SEBLUP&amp;nbsp; (Spatial Empirical Best Linear Unbiased Prediction) method. Using data from SUSENAS, PODES, and some publication from BPS, the main objective of this study is to estimate the mean years school at kecamatan level in Kabupaten Bogor using EBLUP, Semiparametric P-Spline approach and SEBLUP method. The results show that based on the RRMSE value, the cubic P-Spline model with three knots predicts the mean years school better than EBLUP. Meanwhile, the addition of spatial effects into the small area estimation has not been able to improve the estimated value of the P-Spline semiparametric approach.</dc:description>
	<dc:publisher xml:lang="en-US">PATTIMURA UNIVERSITY</dc:publisher>
	<dc:date>2022-12-15</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/6837</dc:identifier>
	<dc:identifier>10.30598/barekengvol16iss4pp1541-1550</dc:identifier>
	<dc:source xml:lang="en-US">BAREKENG: Jurnal Ilmu Matematika dan Terapan; Vol 16 No 4 (2022): BAREKENG: Journal of Mathematics and Its Applications; 1541-1550</dc:source>
	<dc:source>2615-3017</dc:source>
	<dc:source>1978-7227</dc:source>
	<dc:source>10.30598/barekengvol16iss4year2022</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/6837/5168</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2022 Christiana Anggraeni Putri, Indahwati Indahwati, Anang Kurnia</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>