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Pemodelan Regresi Binomial Negatif Bivariat pada Data Jumlah Kematian Ibu dan Bayi di Provinsi Sulawesi Selatan Tahun 2020

ESTIMASI: Journal of Statistics and Its Application · Vol. 4 No. 1 (2023) · pp. 78–88

DOI
10.20956/ejsa.vi.25082
ISSN
2721-379X, 2721-3803
Published
2023-02-14
Publisher
Hasanuddin University
Language
en

Abstract

In general, negative binomial regression is used for univariate discrete data that is overdispersive and follows the Poisson distribution. In the real world, a case is often influenced by two discrete variables that are correlated with each other. Therefore, in this paper we will examine the regression that is influenced by two independent variables, has overdispersion properties and follows a bivariate Poisson distribution. This regression is called bivariate negative binomial regression with model parameters estimated using the Maximum Likelihood Estimation (MLE) method and Newton Raphson iterations. The formation of this model is based on the Famoye method, while in general it uses the Cheon method. Furthermore, the results of this study were applied to data on the number of maternal and infant deaths in South Sulawesi Province in 2020. The results obtained were the number of puskesmas that had a significant effect on the number of maternal deaths and the proportion of handling obstetric complications, the proportion of pregnant women implementing the K4 program, the proportion of deliveries in facilities health services, the proportion of postpartum mothers implementing the KF2 program and the number of puskesmas have a significant effect on the number of infant deaths.

Keywords

Collaboration network of the authors

40 authors · 91 co-authorship links · 1 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
  • Lampung University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Nasrah SirajangHasanuddin University6
Nurtiti SunusiHasanuddin University5
RaupongAffiliation unknown2
Saleh AfAffiliation unknown2
Sitti SahrimanHasanuddin University2
Ika FitrianaHasanuddin University2
Andi Kresna JayaHasanuddin University2
Aqilah Salsabila RahmanAffiliation unknown2
Sonya SonyaAffiliation unknown2
Alimatun NajihaHasanuddin University2
Aidawayati RangkutiHasanuddin University2
Iis Cendrah KasihHasanuddin University1
Rizki AdiputraHasanuddin University1
Asti Inayati MagfirahAffiliation unknown1
Mega Selvia TjahayaAffiliation unknown1
Andi Isna YunitaHasanuddin University1
Ratmila MammiAffiliation unknown1
Nur Alfianingsih RajaHasanuddin University1
Riska RasyidHasanuddin University1
Hendriete Tiur Marowi WaibusiHasanuddin University1
Anisa AnisaAffiliation unknown1
Victor LimanAffiliation unknown1
Sitti SahrimanAffiliation unknown1
Rael Hofni TandirerungAffiliation unknown1
Anisa KalondengHasanuddin University1
Surya Cahyadi SyamAffiliation unknown1
Nuralyatussa’ AdaAffiliation unknown1
Siswanto SiswantoHasanuddin University1
Taufiq AkbarHasanuddin University1
Fadhila Febriyanti NajamuddinHasanuddin University1
Mayashari MayashariHasanuddin University1
Anisa AnisaHasanuddin University1
Mustabsyirah MustabsyirahHasanuddin University1
Agung Muhammad TakdirHasanuddin University1
Sri Astuti ThamrinHasanuddin University1
Nuravia NuraviaAffiliation unknown1
Fitriani FitrianiLampung 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 Binomial Negatif Bivariat pada Data Jumlah Kematian Ibu dan Bayi di Provinsi Sulawesi Selatan Tahun 2020</dc:title>
	<dc:creator xml:lang="en">Nurhidaya L</dc:creator>
	<dc:creator xml:lang="en">Herdiani, Erna Tri</dc:creator>
	<dc:creator xml:lang="en">Tinungki, Georgina Maria</dc:creator>
	<dc:subject xml:lang="en">Bivariate Negative Binomial Distribution</dc:subject>
	<dc:subject xml:lang="en">Bivariate Negative Binomial Regression</dc:subject>
	<dc:subject xml:lang="en">MLE</dc:subject>
	<dc:subject xml:lang="en">Newton Rapshon</dc:subject>
	<dc:subject xml:lang="en">Overdispersion</dc:subject>
	<dc:description xml:lang="en">In general, negative binomial regression is used for univariate discrete data that is overdispersive and follows the Poisson distribution. In the real world, a case is often influenced by two discrete variables that are correlated with each other. Therefore, in this paper we will examine the regression that is influenced by two independent variables, has overdispersion properties and follows a bivariate Poisson distribution. This regression is called bivariate negative binomial regression with model parameters estimated using the Maximum Likelihood Estimation (MLE) method and Newton Raphson iterations. The formation of this model is based on the Famoye method, while in general it uses the Cheon method. Furthermore, the results of this study were applied to data on the number of maternal and infant deaths in South Sulawesi Province in 2020. The results obtained were the number of puskesmas that had a significant effect on the number of maternal deaths and the proportion of handling obstetric complications, the proportion of pregnant women implementing the K4 program, the proportion of deliveries in facilities health services, the proportion of postpartum mothers implementing the KF2 program and the number of puskesmas have a significant effect on the number of infant deaths.</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/25082</dc:identifier>
	<dc:identifier>10.20956/ejsa.vi.25082</dc:identifier>
	<dc:source xml:lang="en">ESTIMASI: Journal of Statistics and Its Application; Vol. 4, No. 1, Januari, 2023 : Estimasi; 78-88</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/25082/9335</dc:relation>
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