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Mengatasi Overdispersi Menggunakan Regresi Binomial Negatif dengan Penaksir Maksimum Likelihood pada Kasus Demam Berdarah di Kota Makassar

ESTIMASI: Journal of Statistics and Its Application · Vol. 5 No. 1 (2024) · pp. 37–51

DOI
10.20956/ejsa.v5i1.14552
ISSN
2721-379X, 2721-3803
Published
2024-01-29
Publisher
Hasanuddin University
Language
en

Abstract

The basic assumption in Poisson regression is that the mean value is the same as the variance value, which is called equidispersion. However, in some cases, this assumption is not met. A variance value that is greater than the average is called overdispersion and is called underdispersion if the variance value is smaller than the average value. So the Poisson regression model is no longer suitable for modeling this type of data because it will produce biased parameter estimates, therefore a negative binomial regression model is used. The research results show that estimating the parameters of the negative binomial regression model uses the maximum likelihood estimation method and then continues with the Newton-Raphson iteration method. The results obtained show that the negative binomial regression model overcomes the overdispersion that occurs in data on the number of dengue fever cases in Makassar City with the model and an AIC value of 236.06647. The negative binomial regression model produces many models and then the best model with the smallest AIC criteria is selected.

Keywords

Collaboration network of the authors

40 authors · 139 co-authorship links · showing the 40 most connected

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Sultan Ageng Tirtayasa University
  • Institut Agama Islam Negeri Kendari
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Anna IslamiyatiAffiliation unknown5
Andi Kresna JayaAffiliation unknown3
Anisa KalondengAffiliation unknown3
A.Muthiah Nur AngrianyAffiliation unknown3
Andi M Alfin BasoAffiliation unknown2
Andi Sitti Fahmi Riyanti HufainiAffiliation unknown2
Nurtiti SunusiAffiliation unknown2
Sitti SahrimanAffiliation unknown2
Asnidar AsnidarAffiliation unknown2
Siswanto SiswantoAffiliation unknown2
Andi Isna YunitaAffiliation unknown2
Zhazha Alifkhamulki RamdhaniAffiliation unknown1
Samsul ArifinAffiliation unknown1
Anisa AnisaSultan Ageng Tirtayasa University1
Bunga ApriliaAffiliation unknown1
Dwicahyo Ramadhan PriyatnaAffiliation unknown1
La Podje TalangkoAffiliation unknown1
Vieri KoerniawanAffiliation unknown1
Grace Oktavia YusufAffiliation unknown1
Dewi Rahma EnteAffiliation unknown1
Andi Tenri Riski AmaliaAffiliation unknown1
Fitri AnnisaAffiliation unknown1
Nurul IkhsaniAffiliation unknown1
Wa Ode Sitti Amni AmniAffiliation unknown1
Nurul HikmahInstitut Agama Islam Negeri Kendari1
Nasrah SirajangAffiliation unknown1
Kevin Ekarinaldo PalunganAffiliation unknown1
Fauziah NurhidayahAffiliation unknown1
Anni Ivoni ParapaAffiliation unknown1
Rini JamalAffiliation unknown1
Andi FebriyantiAffiliation unknown1
Rahmiati RahimAffiliation unknown1
A. Elisha WulandariAffiliation unknown1
Belia Nurul FajriAffiliation unknown1
Abdullah PannuAffiliation unknown1
Alfian Akbar VallarinoAffiliation unknown1
Anugrah Nur Isnaeni RahmanAffiliation 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">Mengatasi Overdispersi Menggunakan Regresi Binomial Negatif dengan Penaksir Maksimum Likelihood pada Kasus Demam Berdarah di Kota Makassar</dc:title>
	<dc:creator xml:lang="en">Fadil, Muhammad</dc:creator>
	<dc:creator xml:lang="en">Raupong, Raupong</dc:creator>
	<dc:creator xml:lang="en">Ilyas, Nirwan</dc:creator>
	<dc:subject xml:lang="en">GLM</dc:subject>
	<dc:subject xml:lang="en">Poisson regression</dc:subject>
	<dc:subject xml:lang="en">Overdispersion</dc:subject>
	<dc:subject xml:lang="en">Negative Binomial Regression Model</dc:subject>
	<dc:subject xml:lang="en">AIC</dc:subject>
	<dc:description xml:lang="en">The basic assumption in Poisson regression is that the mean value is the same as the variance value, which is called equidispersion. However, in some cases, this assumption is not met. A variance value that is greater than the average is called overdispersion and is called underdispersion if the variance value is smaller than the average value. So the Poisson regression model is no longer suitable for modeling this type of data because it will produce biased parameter estimates, therefore a negative binomial regression model is used. The research results show that estimating the parameters of the negative binomial regression model uses the maximum likelihood estimation method and then continues with the Newton-Raphson iteration method. The results obtained show that the negative binomial regression model overcomes the overdispersion that occurs in data on the number of dengue fever cases in Makassar City with the model   and an AIC value of 236.06647. The negative binomial regression model produces many models and then the best model with the smallest AIC criteria is selected.</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/14552</dc:identifier>
	<dc:identifier>10.20956/ejsa.v5i1.14552</dc:identifier>
	<dc:source xml:lang="en">ESTIMASI: Journal of Statistics and Its Application; Vol. 5, No. 1, Januari, 2024 : Estimasi; 37-51</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/14552/11182</dc:relation>
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