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CLUSTER FAST DOUBLE BOOTSTRAP APPROACH WITH RANDOM EFFECT SPATIAL MODELING

BAREKENG: Jurnal Ilmu Matematika dan Terapan · Vol. 17 No. 2 (2023) · pp. 0945–0954

DOI
10.30598/barekengvol17iss2pp0945-0954
ISSN
2615-3017, 1978-7227
Published
2023-06-11
Publisher
PATTIMURA UNIVERSITY
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Panel data is a combination of cross-sectional and time series data. Spatial panel analysis is an analysis to obtain information based on observations affected by the space or location effects. The effect of location effects on spatial analysis is presented in the form of weighting. The use of panel data in spatial regression provides a number of advantages, however, the spatial dependence test and parameter estimators generated in the spatial regression of data panel will be inaccurate when applied to areas with a small number of spatial units. One method to overcome the problem of small spatial unit size is the bootstrap method. This study used the fast double bootstrap (FDB) method by modeling the poverty rate in the Flores islands. The data used in the study was sourced from the BPS NTT Province website. The results of Hausman test show that the right model is Random effect. The spatial dependence test concludes that there is a spatial dependence and the poverty modeling in the Flores islands tends to use the SAR model. SAR random effect model R2 shows the value of 77.38 percent and it does not meet the assumption of normality. Spatial Autoregressive Random effect model with the Fast Double Bootstrap approach is able to explain the diversity of poverty rate in the Flores Island by 99.83 percent and fulfilling the assumption of residual normality. The results of the analysis using the FDB approach on the spatial panel show better results than the common spatial panel.

Collaboration network of the authors

40 authors · 126 co-authorship links · 20 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.

  • University of Brawijaya
  • Universitas Negeri Surabaya
  • Universitas Papua
  • State University of Malang
  • University of Bengkulu
  • Université d’Antsiranana
  • University of Pembangunan Nasional Veteran Jawa Timur
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Atiek IrianyUniversity of Brawijaya5
Suci AstutikUniversity of Brawijaya5
Eni SumarminingsihUniversity of Brawijaya2
Danang AriyantoUniversitas Negeri Surabaya2
Abdi Negara GuciUniversity of Brawijaya2
Viky Iqbal Azizul AlimState University of Malang2
Laila Nur AzizahUniversity of Brawijaya2
Meirida Karisma PutriUniversity of Brawijaya2
Achmad EfendiUniversity of Brawijaya2
Sativandi RizaUniversity of Brawijaya2
Rinda Lolita MelanwatiUniversity of Brawijaya1
Adji FernandesAffiliation unknown1
SolimunAffiliation unknown1
Dea Saraswati PramaningrumUniversity of Brawijaya1
Fachira Haneinanda JuniantoUniversity of Brawijaya1
Nabila Ayunda SoviaAffiliation unknown1
Luthfatul AmalianaAffiliation unknown1
Rossanda Sevia GadisAffiliation unknown1
Naurah Atikah RabbaniAffiliation unknown1
Novi Nur AiniUniversity of Brawijaya1
Sri HandayaniUniversity of Brawijaya1
Nur Silviyah RahmiUniversity of Brawijaya1
Alifiandi Rafi MuhammadUniversity of Brawijaya1
Ulfah MaisarohUniversity of Brawijaya1
Dahlia Gladiola Rurina MenufanduUniversitas Papua1
Danang AriantoUniversity of Brawijaya1
Arditama PutraUniversity of Brawijaya1
Devi Azarina Manzilir RohmahUniversity of Brawijaya1
Fachri FaisalUniversity of Bengkulu1
Adji Achmad Rinaldo FernandesUniversity of Brawijaya1
Armando Jacquis Federal ZamelinaUniversity of Brawijaya1
Lucius RamifidisoaUniversité d’Antsiranana1
Solimun SolimunUniversity of Brawijaya1
Hafizh Syihabuddin Al JauharUniversity of Brawijaya1
Naufal Shela AbdilaUniversity of Brawijaya1
Muhamad Liswansyah PratamaUniversity of Pembangunan Nasional Veteran Jawa Timur1

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 8 Nov 2026.
Original metadata (oai_dc)
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	<dc:title xml:lang="en-US">CLUSTER FAST DOUBLE BOOTSTRAP APPROACH WITH RANDOM EFFECT SPATIAL MODELING</dc:title>
	<dc:creator>Ngabu, Wigbertus</dc:creator>
	<dc:creator>Fitriani, Rahma</dc:creator>
	<dc:creator>Pramoedyo, Henny</dc:creator>
	<dc:creator>Astuti, Ani Budi</dc:creator>
	<dc:description xml:lang="en-US">Panel data is a combination of cross-sectional and time series data. Spatial panel analysis is an analysis to obtain information based on observations affected by the space or location effects. The effect of location effects on spatial analysis is presented in the form of weighting. The use of panel data in spatial regression provides a number of advantages, however, the spatial dependence test and parameter estimators generated in the spatial regression of data panel will be inaccurate when applied to areas with a small number of spatial units. One method to overcome the problem of small spatial unit size is the bootstrap method. This study used the fast double bootstrap (FDB) method by modeling the poverty rate in the Flores islands. The data used in the study was sourced from the BPS NTT Province website. The results of Hausman test show that the right model is Random effect. The spatial dependence test concludes that there is a spatial dependence and the poverty modeling in the Flores islands tends to use the SAR model. SAR random effect model R2 shows the value of 77.38 percent and it does not meet the assumption of normality. Spatial Autoregressive Random effect model with the Fast Double Bootstrap approach is able to explain the diversity of poverty rate in the Flores Island by 99.83 percent and fulfilling the assumption of residual normality. The results of the analysis using the FDB approach on the spatial panel show better results than the common spatial panel. </dc:description>
	<dc:publisher xml:lang="en-US">PATTIMURA UNIVERSITY</dc:publisher>
	<dc:date>2023-06-11</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/8025</dc:identifier>
	<dc:identifier>10.30598/barekengvol17iss2pp0945-0954</dc:identifier>
	<dc:source xml:lang="en-US">BAREKENG: Jurnal Ilmu Matematika dan Terapan; Vol 17 No 2 (2023): BAREKENG: Journal of Mathematics and Its Applications; 0945-0954</dc:source>
	<dc:source>2615-3017</dc:source>
	<dc:source>1978-7227</dc:source>
	<dc:source>10.30598/barekengvol17iss2year2023</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/8025/5955</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2023 Wigbertus Ngabu, Rahma Fitriani, Henny Pramoedyo, Ani Budi Astuti</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>