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Journals / Hacettepe Journal of Mathematics and Statistics / 2010 / Cilt: 39 - Sayı: 3

Application of MML methodology to an $alpha$ series process with Weibull distribution

Halil AYDOĞDU
Birdal SENOĞLU
Mahnut KARA
Pages
449–460
Publication Date
2022-10-18
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

We also apply the MML methodol- ogy to two real life data sets to show the performance of the MML estimators compared to the NP estimators.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In an $alpha$ -series process, explicit estimators of the parameters $alpha$ , $mu$ and $sigma^2$ are obtained by using the methodology of modified maximum likelihood (MML) when the distribution of the first occurrence time of an event is assumed to be Weibull.
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Key Findings & Data:Monte Carlo simulations are performed to compare the efficiencies of the MML estimators with the corresponding nonparametric (NP) estimators.
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Conclusion & Impact:We also apply the MML methodol- ogy to two real life data sets to show the performance of the MML estimators compared to the NP estimators.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

In an $alpha$ -series process, explicit estimators of the parameters $alpha$ , $mu$ and $sigma^2$ are obtained by using the methodology of modified maximum likelihood (MML) when the distribution of the first occurrence time of an event is assumed to be Weibull. Monte Carlo simulations are performed to compare the efficiencies of the MML estimators with the corresponding nonparametric (NP) estimators. We also apply the MML methodol- ogy to two real life data sets to show the performance of the MML estimators compared to the NP estimators.

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Cite This Article

AYDOĞDU, H., SENOĞLU, B., KARA, M. (2022). Application of MML methodology to an $alpha$ series process with Weibull distribution. Hacettepe Journal of Mathematics and Statistics, Cilt: 39 - Sayı: 3, 449–460. https://ajindex.com/articles/530650

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