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PARAMETER ESTIMATION OF SPATIAL REGRESSION MODEL WITH GEOGRAPHICALLY WEIGHTED POISSON REGRESSION METHOD

Unnes Journal of Mathematics · Vol. 8 No. 2 (2019) · hlm. 21–31

DOI
10.15294/ujm.v8i2.23796
ISSN
2460-5859, 2252-6943
Terbit
2019-08-17
Penerbit
Universitas Negeri Semarang
Bahasa
eng

Abstrak

Poisson Regression is one of the non-linear regression analysis whose Poisson distributed response variable.Geographically Weighted Poisson Regression (GWPR) is one of the statistical methods to analyze spatial data with point approach. The purpose of this research is to form GWPR model with fixed bisquare and adaptive bisquare kernel function, and compare best model of GWPR with kernel fixed bisquare and adaptive bisquare function. The data of this research is the percentage of poor people in Central Java Province. In this study there are seven (7) variables related to the percentage factor of the poverty population. The test obtained 2 significant variables are population life expectancy and income per-capita population has been adjusted.Based on the result of research, it is found that GWPR model is more suitable than Poisson regression. Provided Geographically Weighted Poisson Regression model with fixed bisquare fixed function and adaptive bisquare globally in Province of Central Java. The advantage of the model can be seen from the value of AIC. The AIC value obtained in the fixed bisquare kernel is 178,7446. Whereas, The AIC value obtained in adaptive kernel bisquare is 183.2349. The GWPR model with the fixed Bisquare kernel is better than GWPR adaptive bisquare.

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Emi PujiastutiState University of Semarang7
Scolastika MarianiState University of Semarang6
Amin SuyitnoState University of Semarang6
Putriaji HendikawatiState University of Semarang4
Masrukan MasrukanState University of Semarang3
Arief AgoestantoAfiliasi tidak diketahui3
Nurkaromah DwidayatiState University of Semarang2
Nur Karomah DwidayatiState University of Semarang2
Walid WalidState University of Semarang2
Aisyah Fany AchmaliaAfiliasi tidak diketahui2
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Isti HidayahState University of Semarang2
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Sumber metadata: OAI-PMH (metadata jurnal) https://journal.unnes.ac.id/sju/index.php/ujm/oai, pertama di-harvest 6 Nov 2026, terakhir terlihat 6 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en-US">PARAMETER ESTIMATION OF SPATIAL REGRESSION MODEL WITH GEOGRAPHICALLY WEIGHTED POISSON REGRESSION METHOD</dc:title>
	<dc:creator>Fadlilah, Itsnaini Munjiyatul</dc:creator>
	<dc:creator>Sugiman, Sugiman</dc:creator>
	<dc:creator>Sunarmi, Sunarmi</dc:creator>
	<dc:description xml:lang="en-US">Poisson Regression is one of the non-linear regression analysis whose Poisson distributed response variable.Geographically Weighted Poisson Regression (GWPR) is one of the statistical methods to analyze spatial data with point approach. The purpose of this research is to form GWPR model with fixed bisquare and adaptive bisquare kernel function, and compare best model of GWPR with kernel fixed bisquare and adaptive bisquare function. The data of this research is the percentage of poor people in Central Java Province. In this study there are seven (7) variables related to the percentage factor of the poverty population. The test obtained 2 significant variables are population life expectancy &amp;nbsp;and income per-capita population has been adjusted .Based on the result of research, it is found that GWPR model is more suitable than Poisson regression. Provided Geographically Weighted Poisson Regression model with fixed bisquare fixed function and adaptive bisquare globally in Province of Central Java . The advantage of the model can be seen from the value of AIC. The AIC value obtained in the fixed bisquare kernel is 178,7446. Whereas, The AIC value obtained in adaptive kernel bisquare is 183.2349. The GWPR model with the fixed Bisquare kernel is better than GWPR adaptive bisquare.</dc:description>
	<dc:publisher xml:lang="en-US">Universitas Negeri Semarang</dc:publisher>
	<dc:date>2019-08-17</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.unnes.ac.id/sju/ujm/article/view/23796</dc:identifier>
	<dc:identifier>10.15294/ujm.v8i2.23796</dc:identifier>
	<dc:source xml:lang="en-US">Unnes Journal of Mathematics; Vol 8 No 2 (2019); 21-31</dc:source>
	<dc:source>2460-5859</dc:source>
	<dc:source>2252-6943</dc:source>
	<dc:source>10.15294/ujm.v8i2</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.unnes.ac.id/sju/ujm/article/view/23796/17365</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2019 Unnes Journal of Mathematics</dc:rights>
</oai_dc:dc>