The Marshall-Olkin Gompertz Distribution: Properties and Applications

Eghwerido, Joseph Thomas; Ogbo, Joel Oruaoghene; Omotoye, Adebola Evelyn

doi:10.6092/issn.1973-2201/10993

Cited by 1 publication

(1 citation statement)

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“…Many researchers studied the MaOBi distributions based on independent and dependent failure conditions. See, for example, Ghitany et al (2007), Ristic and Kundu (2015), Cai et al (2017), Eghwerido et al (2021) and Barnwal and Panwar (2022).…”

Section: Introductionmentioning

confidence: 99%

Statistical inference for marshall-olkin bivariate Kumaraswamy distribution under adaptive progressive hybrid censored dependent competing risks data

Haghverdi Vardani,

Panahi,

Behzadi

2024

Phys. Scr.

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The statistical inference under competing risks model is of great significance in reliability analysis and it is more practical to assume that they have dependent competing causes of failure in actual situations. In this article, we make inference for unknown parameters of a Marshall-Olkin bivariate Kumaraswamy distribution under adaptive progressive hybrid censoring mechanism. The maximum likelihood estimations of the unknown parameters are derived, and the Fisher information matrix is then employed to construct asymptotic confidence intervals. Bayes estimates are evaluated against squared error and linex loss functions assuming ordered Gamma-Dirichlet and Gamma-Dirichlet prior distributions for order restriction and without order restriction cases respectively. The Metropolis-Hasting and Lindley techniques are applied to acquire the estimates of all unknown parameters. A thorough simulation analysis is demonstrated to assess the performance of the supplied approaches across various sample sizes. The usefulness of the techniques is illustrated using real engineering data to prove their versatility in practical applications.

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