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ANALISIS SURVIVAL UNTUK PARAMETER SKALA DARI DISTRIBUSI WEIBULL MENGGUNAKAN MLE DAN METODE BAYESIAN

BAREKENG: Jurnal Ilmu Matematika dan Terapan · Vol. 15 No. 1 (2021) · pp. 147–156

DOI
10.30598/barekengvol15iss1pp147-156
ISSN
2615-3017, 1978-7227
Published
2021-03-01
Publisher
PATTIMURA UNIVERSITY
Language
eng

Abstract

Modeling of survival data is necessary and important to do. Survival data is generally assumed to have a Weibull distribution. Bayesian approach has been implemented to estimate the parameter in such this survival analysis. This study purposes to compare the performance of the Maximum Likelihood and Bayesian using Invers Gamma as prior conjugate for estimating the survival function of scale parameter of Weibull distribution. The comparisons are made through simulation study. The best performance of both estimators is chosen based on the lowest value of absolute bias and the mean square error. Two different size samples are generated to illustrate the life time data which are used in this study. This study results that maximum likelihood is the best estimator compared to Bayes with Invers Gamma distribution as conjugate prior.

Collaboration network of the authors

10 authors · 19 co-authorship links

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  • Universitas Muhammadiyah Madiun
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Dodi DeviantoAffiliation unknown4
Frilianda WulandariAffiliation unknown1
Rudiyanto RudiyantoAffiliation unknown1
Maiyastri MaiyastriAffiliation unknown1
Lilis Harianti HasibuanAffiliation unknown1
Cici SaputriAffiliation unknown1
Muhammad Qolbi ShobriUniversitas Muhammadiyah Madiun1

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://ojs3.unpatti.ac.id/index.php/barekeng/oai, first harvested 5 Nov 2026, last seen 5 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">ANALISIS SURVIVAL UNTUK PARAMETER SKALA DARI DISTRIBUSI WEIBULL MENGGUNAKAN MLE DAN METODE BAYESIAN</dc:title>
	<dc:creator>Yanuar, Ferra</dc:creator>
	<dc:creator>Wulandari, Sisca</dc:creator>
	<dc:creator>Rahmi HG, Izzati</dc:creator>
	<dc:description xml:lang="en-US">Modeling of survival data is necessary and important to do. Survival data is generally assumed to have a Weibull distribution. Bayesian approach has been implemented to estimate the parameter in such this survival analysis. This study purposes to compare the performance of the Maximum Likelihood and Bayesian using Invers Gamma as prior conjugate for estimating the survival function of scale parameter of Weibull distribution. The comparisons are made through simulation study. The best performance of both estimators is chosen based on the lowest value of absolute bias and the mean square error. Two different size samples are generated to illustrate the life time data which are used in this study. This study results that maximum likelihood is the best estimator compared to Bayes with Invers Gamma distribution as conjugate prior.</dc:description>
	<dc:publisher xml:lang="en-US">PATTIMURA UNIVERSITY</dc:publisher>
	<dc:date>2021-03-01</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://ojs3.unpatti.ac.id/index.php/barekeng/article/view/3109</dc:identifier>
	<dc:identifier>10.30598/barekengvol15iss1pp147-156</dc:identifier>
	<dc:source xml:lang="en-US">BAREKENG: Jurnal Ilmu Matematika dan Terapan; Vol 15 No 1 (2021): BAREKENG: Jurnal Ilmu Matematika dan Terapan; 147-156</dc:source>
	<dc:source>2615-3017</dc:source>
	<dc:source>1978-7227</dc:source>
	<dc:source>10.30598/barekengvol15iss1year2021</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/3109/2705</dc:relation>
</oai_dc:dc>