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Estimasi Parameter Model Poisson Hidden Markov Pada Data Banyaknya Kedatangan Klaim Asuransi Jiwa

ESTIMASI: Journal of Statistics and Its Application · Vol. 1 No. 2 (2022) · pp. 65–73

DOI
10.20956/ejsa.v1i2.9302
ISSN
2721-379X, 2721-3803
Published
2022-02-02
Publisher
Hasanuddin University
Language
en

Abstract

The Poisson hidden Markov model is a model that consists of two parts. The first part is the cause of events that are hidden or cannot be observed directly and form a Markov chain, while the second part is the process of observation or observable parts that depend on the cause of the event and following the Poisson distribution. The Poisson hidden Markov model parameters are estimated using the Maximum Likelihood Estimator (MLE). But it is difficult to find analytical solutions from the ln-likelihood function. Therefore, the Expectation Maximization (EM) algorithm is used to obtain its numerical solutions which are then applied to life insurance data. The best model is obtained with 2 states or m = 2 based on the smallest Bayesian Information Criterion (BIC) value of 338,778 and the average predicted number of claims arrivals is 0.385 per day.

Keywords

Collaboration network of the authors

33 authors · 97 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
Anna IslamiyatiAffiliation unknown4
Nirwan IlyasAffiliation unknown4
A.Muthiah Nur AngrianyAffiliation unknown2
Andi Kresna JayaAffiliation unknown2
Zhazha Alifkhamulki RamdhaniAffiliation unknown1
Samsul ArifinAffiliation unknown1
Andi Sitti Fahmi Riyanti HufainiAffiliation unknown1
Dwicahyo Ramadhan PriyatnaAffiliation unknown1
La Podje TalangkoAffiliation unknown1
Dewi Rahma EnteAffiliation unknown1
Andi Tenri Riski AmaliaAffiliation unknown1
Novilia JaoAffiliation unknown1
Sitti SahrimanAffiliation unknown1
Fitri AnnisaAffiliation unknown1
Muhammad FadilAffiliation unknown1
Erna Tri HerdianiAffiliation unknown1
Riska RasyidAffiliation unknown1
Ika Pratiwi HayaAffiliation unknown1
Asnidar AsnidarAffiliation unknown1
Nurul HikmahInstitut Agama Islam Negeri Kendari1
Nasrah SirajangAffiliation unknown1
Fauziah NurhidayahAffiliation unknown1
Anni Ivoni ParapaAffiliation unknown1
- SiswantoAffiliation unknown1
Andi Isna YunitaAffiliation unknown1
Ayu Pratika PutriAffiliation unknown1
Annurial Fitrayah TaufiqAffiliation unknown1
Ahmad Maulana SyarifAffiliation unknown1
Nurmimie KharinaAffiliation unknown1
Samsir Aditya AniaAffiliation 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">Estimasi Parameter Model Poisson Hidden Markov Pada Data Banyaknya Kedatangan Klaim Asuransi Jiwa</dc:title>
	<dc:creator xml:lang="en">Koerniawan, Vieri</dc:creator>
	<dc:creator xml:lang="en">Sunusi, Nurtiti</dc:creator>
	<dc:creator xml:lang="en">Raupong, Raupong</dc:creator>
	<dc:subject xml:lang="en">Poisson Hidden Markov Model</dc:subject>
	<dc:subject xml:lang="en">MLE</dc:subject>
	<dc:subject xml:lang="en">EM Algorithm</dc:subject>
	<dc:subject xml:lang="en">Life Insurance</dc:subject>
	<dc:subject xml:lang="en">BIC</dc:subject>
	<dc:description xml:lang="en">The Poisson hidden Markov model is a model that consists of two parts. The first part is the cause of events that are hidden or cannot be observed directly and form a Markov chain, while the second part is the process of observation or observable parts that depend on the cause of the event and following the Poisson distribution. The Poisson hidden Markov model parameters are estimated using the Maximum Likelihood Estimator (MLE). But it is difficult to find analytical solutions from the ln-likelihood function. Therefore, the Expectation Maximization (EM) algorithm is used to obtain its numerical solutions which are then applied to life insurance data. The best model is obtained with 2 states or m = 2 based on the smallest Bayesian Information Criterion (BIC) value of 338,778 and the average predicted number of claims arrivals is 0.385 per day.</dc:description>
	<dc:publisher xml:lang="en">Hasanuddin University</dc:publisher>
	<dc:date>2022-02-02</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/9302</dc:identifier>
	<dc:identifier>10.20956/ejsa.v1i2.9302</dc:identifier>
	<dc:source xml:lang="en">ESTIMASI: Journal of Statistics and Its Application; Vol. 1, No. 2, Juli, 2020 : Estimasi; 65-73</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/9302/6796</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2020 ESTIMASI: Journal of Statistics and Its Application</dc:rights>
</oai_dc:dc>