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INVERSE GAUSSIAN REGRESSION MODELING AND ITS APPLICATION IN NEONATAL MORTALITY CASES IN INDONESIA

BAREKENG: Jurnal Ilmu Matematika dan Terapan · Vol. 16 No. 4 (2022) · pp. 1197–1206

DOI
10.30598/barekengvol16iss4pp1197-1206
ISSN
2615-3017, 1978-7227
Published
2022-12-15
Publisher
PATTIMURA UNIVERSITY
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Inverse Gaussian Regression (IGR) is a suitable model for modeling positively skewed response data, which follows the inverse Gaussian distribution. The IGR model was formed from the Generalized Linear Models (GLM). This study aims to model the IGR with applied to model the factors influencing the infant mortality cases of provinces in Indonesia. Estimation of the IGR model parameters was employed by the Maximum Likelihood Estimation (MLE) and Fisher scoring methods. The Likelihood Ratio Test (LRT) and Wald test were used for hypothesis testing of significance parameters. The IGR model was applied to the infant mortality cases of provinces in Indonesia in 2020. The data for modeling infant mortality cases using IGR were obtained from the Ministry of Health of the Republic of Indonesia and the Central Bureau of Statistics. The result shows that the factors influencing the infant mortality cases of provinces in Indonesia based on the IGR model were: the percentage of pregnant women who received blood-boosting tablets, the percentage of low birth weight, the percentage of complete neonatal visits (KN3), the percentage of toddlers who received early initiation of breastfeeding, the percentage of toddlers who are exclusively breastfeeding, the percentage of toddlers who received complete primary immunization, the percentage of households with access to adequate drinking water, and the percentage of households with access to appropriate sanitation.

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5 authors · 6 co-authorship links

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Co-authorOrganisationJoint articles
Sri WahyuningsihAffiliation unknown1
Yusrian PalilingAffiliation unknown1
Rito GoejantoroAffiliation unknown1
Ardiman WiranataAffiliation 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">INVERSE GAUSSIAN REGRESSION MODELING AND ITS APPLICATION IN NEONATAL MORTALITY CASES  IN INDONESIA</dc:title>
	<dc:creator>Fathurahman, M.</dc:creator>
	<dc:description xml:lang="en-US">Inverse Gaussian Regression (IGR) is a suitable model for modeling positively skewed response data, which follows the inverse Gaussian distribution. The IGR model was formed from the Generalized Linear Models (GLM). This study aims to model the IGR with applied to model the factors influencing the infant mortality cases of provinces in Indonesia. Estimation of the IGR model parameters was employed by the Maximum Likelihood Estimation (MLE) and Fisher scoring methods. The Likelihood Ratio Test (LRT) and Wald test were used for hypothesis testing of significance parameters. The IGR model was applied to the infant mortality cases of provinces in Indonesia in 2020. The data for modeling infant mortality cases using IGR were obtained from the Ministry of Health of the Republic of Indonesia and the Central Bureau of Statistics. The result shows that the factors influencing the infant mortality cases of provinces in Indonesia based on the IGR model were: the percentage of pregnant women who received blood-boosting tablets, the percentage of low birth weight, the percentage of complete neonatal visits (KN3), the percentage of toddlers who received early initiation of breastfeeding, the percentage of toddlers who are exclusively breastfeeding, the percentage of toddlers who received complete primary immunization, the percentage of households with access to adequate drinking water, and the percentage of households with access to appropriate sanitation.</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/6067</dc:identifier>
	<dc:identifier>10.30598/barekengvol16iss4pp1197-1206</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; 1197-1206</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/6067/5136</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2022 M. Fathurahman</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>