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THE USE OF PENALIZED WEIGHTED LEAST SQUARE TO OVERCOME CORRELATIONS BETWEEN TWO RESPONSES

BAREKENG: Jurnal Ilmu Matematika dan Terapan · Vol. 16 No. 4 (2022) · pp. 1497–1504

DOI
10.30598/barekengvol16iss4pp1497-1504
ISSN
2615-3017, 1978-7227
Published
2022-12-15
Publisher
PATTIMURA UNIVERSITY
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

The non-parametric regression model can consider two correlated responses. However, for these conditions, we cannot use the usual estimation process because there are violations of assumptions. To solve this problem, we use a penalized weighted least square involving knots, smoothing parameters, and weighting in the estimation criteria simultaneously. The estimation process involves a weighted criteria matrix in the estimation criteria. Estimation results show that the estimated two-response non-parametric regression function with penalized spline is a linear estimation class in y response observation and depends on the knot point and smoothing parameter. Furthermore, the use of the model on toddler growth data shows some changes in the pattern of weight and height gain. The pattern segmentation that experienced a gradual increase was age 7-43 months for weight and age 6-54 months for height

Collaboration network of the authors

40 authors · 119 co-authorship links · 5 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.

  • Universitas Islam Negeri Raden Fatah Palembang
  • Sultan Ageng Tirtayasa University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Raupong RaupongAffiliation unknown12
Alfian NoorAffiliation unknown11
Maming MamingAffiliation unknown7
Andi Kresna JayaAffiliation unknown6
Maming GaffarAffiliation unknown5
Erna Tri HerdianiAffiliation unknown5
Saleh AFAffiliation unknown4
Sri Astuti ThamrinAffiliation unknown3
Indah RayaAffiliation unknown3
Nirwan IlyasAffiliation unknown3
Georgina Maria TinungkiAffiliation unknown3
Anisa KalondengAffiliation unknown3
Siswanto SiswantoAffiliation unknown3
Prastawa BudiAffiliation unknown2
Andi Tenrisa’naAffiliation unknown2
Seniwati DaliAffiliation unknown2
L. Musa RamangAffiliation unknown2
Widya Nauli Amalia PuteriAffiliation unknown2
Bunga ApriliaAffiliation unknown2
Nurtiti SunusiAffiliation unknown2
Sitti SahrimanAffiliation unknown2
Deby NovianiUniversitas Islam Negeri Raden Fatah Palembang1
Winda Rahayu FitrianiUniversitas Islam Negeri Raden Fatah Palembang1
Cinta MellindaUniversitas Islam Negeri Raden Fatah Palembang1
Feni JuniatiUniversitas Islam Negeri Raden Fatah Palembang1
Muhammad Win AfganiUniversitas Islam Negeri Raden Fatah Palembang1
Heni PujiastutiSultan Ageng Tirtayasa University1
Hepsi NindiasariSultan Ageng Tirtayasa University1
Zhazha Alifkhamulki RamdhaniAffiliation unknown1
Samsul ArifinAffiliation unknown1
Iis Cendrah KasihAffiliation unknown1
Dewi Rahma EnteAffiliation unknown1
Azman AzmanAffiliation 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">THE USE OF PENALIZED WEIGHTED LEAST SQUARE TO OVERCOME CORRELATIONS BETWEEN TWO RESPONSES</dc:title>
	<dc:creator>Islamiyati, Anna</dc:creator>
	<dc:creator>Anisa, Anisa</dc:creator>
	<dc:creator>Zakir, Muhammad</dc:creator>
	<dc:creator>Sirajang, Nasrah</dc:creator>
	<dc:creator>Sari, Ummi</dc:creator>
	<dc:creator>Affan, Fajar</dc:creator>
	<dc:creator>Usrah, Muhammad Jayzul</dc:creator>
	<dc:description xml:lang="en-US">The non-parametric regression model can consider two correlated responses. However, for these conditions, we cannot use the usual estimation process because there are violations of assumptions. To solve this problem, we use a penalized weighted least square involving knots, smoothing parameters, and weighting in the estimation criteria simultaneously. The estimation process involves a weighted criteria matrix &amp;nbsp;in the estimation criteria. Estimation results show that the estimated two-response non-parametric regression function with penalized spline is a linear estimation class in y response observation and depends on the knot point and smoothing parameter. Furthermore, the use of the model on toddler growth data shows some changes in the pattern of weight and height gain. The pattern segmentation that experienced a gradual increase was age 7-43 months for weight and age 6-54 months for height</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/6746</dc:identifier>
	<dc:identifier>10.30598/barekengvol16iss4pp1497-1504</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; 1497-1504</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/6746/5164</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2022 Anna Islamiyati, Anisa Anisa, Muhammad Zakir, Nasrah Sirajang, Ummi Sari, Fajar Affan, Muhammad Jayzul Usrah</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>