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Peta Kendali Atribut Menggunakan Zero-Inflated Generalized Poisson

ESTIMASI: Journal of Statistics and Its Application · Vol. 4 No. 2 (2023) · pp. 176–184

DOI
10.20956/ejsa.v4i2.12932
ISSN
2721-379X, 2721-3803
Published
2023-08-04
Publisher
Hasanuddin University
Language
en

Abstract

If the variable is a discrete random variable with Poisson distribution, the data analysis must fulfill the equidispersion assumption. In reality, these assumptions are not fulfilled because the variance is greater than the mean which is called overdispersion. Overdispersion in data can occur due to the proportion of excess zero values in these variables. To estimate the parameters, the MLE method can be used on data that has a certain distribution by maximizing the likelihood function, it obtained is implicit or nonlinear so that it cant be solved analytically. To get the numerical solution, it solved by using the EM algorithm. The estimation results of the ZIGP distribution parameters are used to create control chart limits for the 2016 Neonatal Mortality Rate data in Makassar with limits of,, and. The chart ARL value is, which is greater than the chart ARL value, which is which indicates that the chart is better at detecting outliers.

Keywords

Collaboration network of the authors

24 authors · 48 co-authorship links

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  • Institut Agama Islam Negeri Kendari
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
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Co-authorOrganisationJoint articles
Georgina Maria TinungkiAffiliation unknown6
Sitti SahrimanAffiliation unknown3
Nuralyatussa’ adaAffiliation unknown2
Iis Cendrah KasihAffiliation unknown1
Rizki AdiputraAffiliation unknown1
Ika FitrianaAffiliation unknown1
Andi Kresna JayaAffiliation unknown1
Reski AmalahAffiliation unknown1
Nur Alfianingsih RajaAffiliation unknown1
Aqilah Salsabila RahmanAffiliation unknown1
Anisa KalondengAffiliation unknown1
Nurkamalia NurkamaliaAffiliation unknown1
Nurtiti SunusiAffiliation unknown1
Riska RasyidAffiliation unknown1
Mukrimin AdamAffiliation unknown1
Nurhidaya LAffiliation unknown1
Sonya SonyaAffiliation unknown1
Rael Hofni TandirerungAffiliation unknown1
Nurul HikmahInstitut Agama Islam Negeri Kendari1
Raupong RaupongAffiliation unknown1
Parida Ayu NingsiAffiliation 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">Peta Kendali Atribut Menggunakan Zero-Inflated Generalized Poisson</dc:title>
	<dc:creator xml:lang="en">Mammi, Ratmila</dc:creator>
	<dc:creator xml:lang="en">Herdiani, Erna Tri</dc:creator>
	<dc:creator xml:lang="en">Sirajang, Nasrah</dc:creator>
	<dc:subject xml:lang="en">Overdispersion</dc:subject>
	<dc:subject xml:lang="en">ZIGP</dc:subject>
	<dc:subject xml:lang="en">EM Algorithm</dc:subject>
	<dc:subject xml:lang="en">ARL</dc:subject>
	<dc:subject xml:lang="en">MLE</dc:subject>
	<dc:description xml:lang="en">If the variable is a discrete random variable with Poisson distribution, the data analysis must fulfill the equidispersion assumption. In reality, these assumptions are not fulfilled because the variance is greater than the mean which is called overdispersion. Overdispersion in data can occur due to the proportion of excess zero values in these variables. To estimate the parameters, the MLE method can be used on data that has a certain distribution by maximizing the likelihood function, it obtained is implicit or nonlinear so that it cant be solved analytically. To get the numerical solution, it solved by using the EM algorithm. The estimation results of the ZIGP distribution parameters are used to create control chart limits for the 2016 Neonatal Mortality Rate data in Makassar with limits of , , and . The  chart ARL value is , which is greater than the chart ARL value, which is  which indicates that the  chart is better at detecting outliers.</dc:description>
	<dc:publisher xml:lang="en">Hasanuddin University</dc:publisher>
	<dc:date>2023-08-04</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/12932</dc:identifier>
	<dc:identifier>10.20956/ejsa.v4i2.12932</dc:identifier>
	<dc:source xml:lang="en">ESTIMASI: Journal of Statistics and Its Application; Vol. 4, No. 2, Juli, 2023 : Estimasi; 176-184</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/12932/10208</dc:relation>
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