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Penerapan Model Regresi Hurdle Binomial Negatif Menggunakan Algoritma Broyden-Fletcher-Goldfarb-Shanno pada Data Jumlah Kematian Bayi di Kota Makassar Tahun 2017

ESTIMASI: Journal of Statistics and Its Application · Vol. 5 No. 1 (2024) · pp. 52–60

DOI
10.20956/ejsa.v5i1.22749
ISSN
2721-379X, 2721-3803
Published
2024-01-29
Publisher
Hasanuddin University
Language
en

Abstract

Poisson regression is a nonlinear regression method used to analyse the relationship between discrete response variables. Equidispersion is the assumption that must be met in the Poisson regression. Furthermore, there are cases in which the equidispersion assumption is invalidated when using the Poisson regression model to analyze data. One such case is overdispersion, which occurs when there is an excess of zero. As a result, the Negative Hurdle Binomial (HBN) regression is implemented to solve the overdispersion issue. Maximum Likelihood Estimation (MLE) with the Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm was applied in this study to perform parameter estimation. In addition, the HBN regression model was used to analyze the data on the number of infant mortality cases in Makassar in 2017 with the variables assumed to be significant with infant mortality. The percentage of infants who were exclusively breastfed was the variable that had a significant impact on the outcome of HBN regression on the data on the number of infant mortality that experienced overdispersion.

Keywords

Collaboration network of the authors

40 authors · 89 co-authorship links · showing the 40 most connected

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  • Sultan Ageng Tirtayasa University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
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Co-authorOrganisationJoint articles
La Podje TalangkoAffiliation unknown4
Nirwan IlyasAffiliation unknown2
Erna Tri HerdianiAffiliation unknown2
Raupong RaupongAffiliation unknown2
Georgina Maria TinungkiAffiliation unknown2
Andi Isna YunitaAffiliation unknown2
Nasrah SirajangAffiliation unknown2
Amalia AndrianingrumAffiliation unknown2
Rusydah KhaeratiAffiliation unknown1
Grace Oktavia YusufAffiliation unknown1
Rizki AdiputraAffiliation unknown1
Risma RismaAffiliation unknown1
Dewi Santika Upa P.Affiliation unknown1
Fitriatusakiah FitriatusakiahAffiliation unknown1
Andi Tenri Riski AmaliaAffiliation unknown1
Ririn AriantiAffiliation unknown1
Muhammad IdmanAffiliation unknown1
Anisa AnisaSultan Ageng Tirtayasa University1
Najiha AlimatunAffiliation unknown1
Reski AmalahAffiliation unknown1
Fitri AnnisaAffiliation unknown1
Nur Fuadil Maqnun WahabAffiliation unknown1
Nurtit SunusiAffiliation unknown1
Mukrimin AdamAffiliation unknown1
Ainul FajriAffiliation unknown1
Nurtiti SunusiAffiliation unknown1
Ika Pratiwi HayaAffiliation unknown1
Anna IslamiyatiAffiliation unknown1
Andi Ummi Melin Aicha AichaAffiliation unknown1
Hendriete Tiur Marowi WaibusiAffiliation unknown1
Rael Hofni TandirerungAffiliation unknown1
Wa Ode Sitti Amni AmniAffiliation unknown1
Nirwan NirwanAffiliation unknown1
Nurhidayatullah NurhidayatullahAffiliation unknown1
Rini JamalAffiliation unknown1
Andi M Alfin BasoAffiliation unknown1
Andi FebriyantiAffiliation 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">Penerapan Model Regresi Hurdle Binomial Negatif Menggunakan Algoritma Broyden-Fletcher-Goldfarb-Shanno pada Data Jumlah Kematian Bayi di Kota Makassar Tahun 2017</dc:title>
	<dc:creator xml:lang="en">Yusuf, Anisa Haura Salsa Fatih </dc:creator>
	<dc:creator xml:lang="en">Jaya, Andi Kresna</dc:creator>
	<dc:creator xml:lang="en">Sahriman, Sitti</dc:creator>
	<dc:subject xml:lang="en">Hurdle Binomial Negatif</dc:subject>
	<dc:subject xml:lang="en">Maximum Likelihood Estimation</dc:subject>
	<dc:subject xml:lang="en">Overdispersion</dc:subject>
	<dc:subject xml:lang="en">excess zero</dc:subject>
	<dc:subject xml:lang="en">the number of infant mortality</dc:subject>
	<dc:description xml:lang="en">Poisson regression is a nonlinear regression method used to analyse the relationship between discrete response variables. Equidispersion is the assumption that must be met in the Poisson regression. Furthermore, there are cases in which the equidispersion assumption is invalidated when using the Poisson regression model to analyze data. One such case is overdispersion, which occurs when there is an excess of zero. As a result, the Negative Hurdle Binomial (HBN) regression is implemented to solve the overdispersion issue. Maximum Likelihood Estimation (MLE) with the Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm was applied in this study to perform parameter estimation. In addition, the HBN regression model was used to analyze the data on the number of infant mortality cases in Makassar in 2017 with the variables assumed to be significant with infant mortality. The percentage of infants who were exclusively breastfed was the variable that had a significant impact on the outcome of HBN regression on the data on the number of infant mortality that experienced overdispersion.</dc:description>
	<dc:publisher xml:lang="en">Hasanuddin University</dc:publisher>
	<dc:date>2024-01-29</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/22749</dc:identifier>
	<dc:identifier>10.20956/ejsa.v5i1.22749</dc:identifier>
	<dc:source xml:lang="en">ESTIMASI: Journal of Statistics and Its Application; Vol. 5, No. 1, Januari, 2024 : Estimasi; 52-60</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/22749/11183</dc:relation>
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