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Pemodelan Mixed Geographically Weighted Regression yang Mengandung Multikolinearitas dengan Regresi Ridge

ESTIMASI: Journal of Statistics and Its Application · Vol. 4 No. 1 (2023) · pp. 100–113

DOI
10.20956/ejsa.vi.25426
ISSN
2721-379X, 2721-3803
Published
2023-02-14
Publisher
Hasanuddin University
Language
en

Abstract

In the Mixed Geographically Weighted Regression (MGWR) model, some variables are local and some are global. In MGWR modeling, it is often found that the data have multicollinearity. To overcome this problem, MGWR models with ridge regression are used. The MGWR model can be applied to poverty cases because it can experience spatial heterogeneity due to differences in geographical, cultural, and economic policies that vary in each region. In this study, the estimation of MGWR model parameters with ridge regression is then applied to data on the poor population of South Sulawesi in 2016. Data on the poor population of South Sulawesi experience multicollinearity, so it is solved using the MGWR model with ridge regression. Variables that have a significant effect globally are x3 and x6. while the variables that have a significant local effect are x2, x4, x5, x7, x8, x9 and x10. The AIC value of the MGWR model with ridge regression of 63.64473 is smaller than the MGWR model, meaning that the addition of ridge regression to the MGWR model makes the model better at overcoming multicollinearity problems.

Keywords

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24 authors · 70 co-authorship links

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Georgina Maria TinungkiAffiliation unknown3
Anna IslamiyatiAffiliation unknown2
Nirwan IlyasAffiliation unknown2
Asti Inayati MagfirahAffiliation unknown1
Mega Selvia TjahayaAffiliation unknown1
Fitri FitriAffiliation unknown1
Nasrah SirajangAffiliation unknown1
Nurkamalia NurkamaliaAffiliation unknown1
Nurul IkhsaniAffiliation unknown1
Surya Cahyadi SyamAffiliation unknown1
Siswanto SiswantoAffiliation unknown1
Afifah Mutiah NurdinAffiliation unknown1
Muh. Indirwan ArfanAffiliation unknown1
Kevin Ekarinaldo PalunganAffiliation unknown1
Muhammad FadilAffiliation unknown1
Andi M Alfin BasoAffiliation unknown1
A. Elisha WulandariAffiliation unknown1
Belia Nurul FajriAffiliation unknown1
Abdullah PannuAffiliation unknown1
Alfian Akbar VallarinoAffiliation unknown1
Anugrah Nur Isnaeni RahmanAffiliation 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">Pemodelan Mixed Geographically Weighted Regression yang  Mengandung Multikolinearitas dengan Regresi Ridge</dc:title>
	<dc:creator xml:lang="en">Suritman</dc:creator>
	<dc:creator xml:lang="en">Raupong</dc:creator>
	<dc:creator xml:lang="en">Kalondeng, Anisa</dc:creator>
	<dc:subject xml:lang="en">Mixed Geographically Weigted Regression</dc:subject>
	<dc:subject xml:lang="en">Multicollinearity</dc:subject>
	<dc:subject xml:lang="en">Poor Population</dc:subject>
	<dc:subject xml:lang="en">Ridge Regression</dc:subject>
	<dc:description xml:lang="en">In the Mixed Geographically Weighted Regression (MGWR) model, some variables are local and some are global. In MGWR modeling, it is often found that the data have multicollinearity. To overcome this problem, MGWR models with ridge regression are used. The MGWR model can be applied to poverty cases because it can experience spatial heterogeneity due to differences in geographical, cultural, and economic policies that vary in each region. In this study, the estimation of MGWR model parameters with ridge regression is then applied to data on the poor population of South Sulawesi in 2016. Data on the poor population of South Sulawesi experience multicollinearity, so it is solved using the MGWR model with ridge regression. Variables that have a significant effect globally are x3 &amp;nbsp;and x6. while the variables that have a significant local effect are x2, x4, x5, x7, x8, x9&amp;nbsp;and x10. The AIC value of the MGWR model with ridge regression of 63.64473 is smaller than the MGWR model, meaning that the addition of ridge regression to the MGWR model makes the model better at overcoming multicollinearity problems.</dc:description>
	<dc:publisher xml:lang="en">Hasanuddin University</dc:publisher>
	<dc:date>2023-02-14</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/25426</dc:identifier>
	<dc:identifier>10.20956/ejsa.vi.25426</dc:identifier>
	<dc:source xml:lang="en">ESTIMASI: Journal of Statistics and Its Application; Vol. 4, No. 1, Januari, 2023 : Estimasi; 100-113</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/25426/9337</dc:relation>
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