Artikel
A study on exponentiated Gompertz distribution under Bayesian discipline using informative priors
The exponentiated Gompertz (EGZ) distribution has been recently used in almost all areas of human endeavours, starting from modelling lifetime data to cancer treatment. Various applications and properties of the EGZ distribution are provided by Anis and De (2020). This paper explores the important properties of the EGZ distribution under Bayesian discipline using two informative priors: the Gamma Prior (GP) and the Inverse Levy Prior (ILP). This is done in the framework of five selected loss functions. The findings show that the two best loss functions are the Weighted Balance Loss Function (WBLF) and the Quadratic Loss Function (QLF). The usefulness of the model is illustrated by the use of real life data in relation to simulated data. The empirical results of the comparison are presented through a graphical illustration of the posterior distributions.
- Language
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Englisch
- Bibliographic citation
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Journal: Statistics in Transition New Series ; ISSN: 2450-0291 ; Volume: 22 ; Year: 2021 ; Issue: 4 ; Pages: 101-119 ; New York: Exeley
- Subject
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exponentiated Gompertz distribution
loss functions
informative priors
Bayes estimators
posterior risks
- Event
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Geistige Schöpfung
- (who)
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Aslam, Muhammad
Afzaal, Mehreen
Bhatti, Muhammad Ishaq
- Event
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Veröffentlichung
- (who)
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Exeley
- (where)
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New York
- (when)
-
2021
- DOI
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doi:10.21307/stattrans-2021-040
- Handle
- Last update
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10.03.2025, 11:42 AM CET
Data provider
This object is provided by:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Artikel
Associated
- Aslam, Muhammad
- Afzaal, Mehreen
- Bhatti, Muhammad Ishaq
- Exeley
Time of origin
- 2021
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