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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

40 authors · 150 co-authorship links · 3 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.

  • Hasanuddin University
  • IPB University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Siswanto SiswantoHasanuddin University5
Raupong RaupongHasanuddin University3
Andi Kresna JayaHasanuddin University2
Anna IslamiyatiHasanuddin University2
Sitti SahrimanHasanuddin University2
M. Zaky Hisyam GozhiHasanuddin University2
Muhammad FadilAffiliation unknown2
Kevin Ekarinaldo PalunganAffiliation unknown2
Andi Sitti Fahmi Riyanti HufainiHasanuddin University1
Grace Oktavia YusufAffiliation unknown1
Fitri FitriAffiliation unknown1
Nasrah SirajangHasanuddin University1
Nurkamalia NurkamaliaAffiliation unknown1
RaupongAffiliation unknown1
SuritmanAffiliation unknown1
Asnidar AsnidarHasanuddin University1
Georgina Maria TinungkiHasanuddin University1
Surya Cahyadi SyamAffiliation unknown1
Afifah Mutiah NurdinHasanuddin University1
Muh. Indirwan ArfanHasanuddin University1
Wa Ode Sitti Amni AmniAffiliation unknown1
Andi M. Alfin BasoHasanuddin University1
A. Elisha WulandariAffiliation unknown1
Belia Nurul FajriAffiliation unknown1
Abdullah PannuIPB University1
Alfian Akbar VallarinoAffiliation unknown1
Anugrah Nur Isnaeni RahmanAffiliation unknown1
Haksar HaksarAffiliation unknown1
Nurtiti SunusiHasanuddin University1
Andi Isna YunitaHasanuddin University1
Ayu Pratika PutriAffiliation unknown1
Annurial Fitrayah TaufiqAffiliation unknown1
Ahmad Maulana SyarifAffiliation unknown1
Nurmimie KharinaAffiliation unknown1
SiswantoAffiliation unknown1
Samsir Aditya AniaAffiliation unknown1
Anisa AnisaHasanuddin University1

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 8 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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