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Pemodelan Regresi Bivariate Poisson Inverse Gaussian pada Kasus Kematian Ibu dan Neonatal di Sulawesi Selatan

ESTIMASI: Journal of Statistics and Its Application · Vol. 4 No. 1 (2023) · pp. 114–121

DOI
10.20956/ejsa.vi.24113
ISSN
2721-379X, 2721-3803
Published
2023-02-14
Publisher
Hasanuddin University
Language
en

Abstract

Overdispersion is a state with a variance value greater than the mean value so the Poisson Inverse Gaussian regression model is used. Meanwhile, to model two correlated response variables, the Bivariate Poisson Inverse Gaussian (BPIG) regression model was used. The BPIG model is a mixed- distributed model between the Poisson Bivariate and Gaussian Inverse distributions. The parameters of the BPIG regression model are estimated using Maximum Likelihood Estimation (MLE) with the Fisher Scoring algorithm. This study was applied to data on the number of maternal and neonatal deaths in South Sulawesi in 2019. The results obtained are predictor variables that affect the number of maternal and neonatal deaths in South Sulawesi in 2019, namely K4 services for pregnant women, active birth control participants, handling obstetric complications, handling neonatal complications and the number of health centers.

Keywords

Collaboration network of the authors

39 authors · 136 co-authorship links

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  • 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 unknown3
Anna IslamiyatiAffiliation unknown3
Andi Kresna JayaAffiliation unknown2
Muhammad FadilAffiliation unknown2
Siswanto SiswantoAffiliation unknown2
Kevin Ekarinaldo PalunganAffiliation unknown2
Anisa AnisaSultan Ageng Tirtayasa University1
Bunga ApriliaAffiliation unknown1
Andi Sitti Fahmi Riyanti HufainiAffiliation unknown1
Grace Oktavia YusufAffiliation unknown1
Fitri FitriAffiliation unknown1
Nasrah SirajangAffiliation unknown1
Nurkamalia NurkamaliaAffiliation unknown1
RaupongAffiliation unknown1
SuritmanAffiliation unknown1
Asnidar AsnidarAffiliation unknown1
Georgina Maria TinungkiAffiliation unknown1
Surya Cahyadi SyamAffiliation unknown1
Afifah Mutiah NurdinAffiliation unknown1
Muh. Indirwan ArfanAffiliation unknown1
Wa Ode Sitti Amni AmniAffiliation 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
Haksar HaksarAffiliation unknown1
- SiswantoAffiliation unknown1
Nurtiti SunusiAffiliation unknown1
Andi Isna YunitaAffiliation unknown1
Ayu Pratika PutriAffiliation unknown1
Annurial Fitrayah TaufiqAffiliation unknown1
Ahmad Maulana SyarifAffiliation unknown1
Nurmimie KharinaAffiliation unknown1
Samsir Aditya AniaAffiliation 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 Regresi Bivariate Poisson Inverse Gaussian pada Kasus Kematian Ibu dan Neonatal di Sulawesi Selatan</dc:title>
	<dc:creator xml:lang="en">Ikhsani, Nurul</dc:creator>
	<dc:creator xml:lang="en">Kalondeng, Anisa</dc:creator>
	<dc:creator xml:lang="en">Ilyas, Nirwan</dc:creator>
	<dc:subject xml:lang="en">BPIG distribution</dc:subject>
	<dc:subject xml:lang="en">BPIG regression</dc:subject>
	<dc:subject xml:lang="en">Fisher Scoring</dc:subject>
	<dc:subject xml:lang="en">MLE</dc:subject>
	<dc:subject xml:lang="en">Overdispersion</dc:subject>
	<dc:description xml:lang="en">Overdispersion is a state with a variance value greater than the mean value so the Poisson Inverse Gaussian regression model is used. Meanwhile, to model two correlated response variables, the Bivariate Poisson Inverse Gaussian (BPIG) regression model was used. The BPIG model is a mixed- distributed model between the Poisson Bivariate and Gaussian Inverse distributions. The parameters of the BPIG regression model are estimated using Maximum Likelihood Estimation (MLE) with the Fisher Scoring algorithm. This study was applied to data on the number of maternal and neonatal deaths in South Sulawesi in 2019. The results obtained are predictor variables that affect the number of maternal and neonatal deaths in South Sulawesi in 2019, namely K4 services for pregnant women , active birth control participants , handling obstetric complications , handling neonatal complications  and the number of health centers .</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/24113</dc:identifier>
	<dc:identifier>10.20956/ejsa.vi.24113</dc:identifier>
	<dc:source xml:lang="en">ESTIMASI: Journal of Statistics and Its Application; Vol. 4, No. 1, Januari, 2023 : Estimasi; 114-121</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/24113/9338</dc:relation>
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