Statistical Inference for the Marshall-Olkin Extended Modified Inverse Rayleigh Distribution Under Adaptive Type-II Progressive Censoring | ||
| Computational Journal of Mathematical and Statistical Sciences | ||
| Article 9, Volume 4, Issue 2, November 2025, Pages 558-576 PDF (580.68 K) | ||
| Document Type: Original Article | ||
| DOI: 10.21608/cjmss.2025.373673.1148 | ||
| Authors | ||
| K Hisham* 1; N A Mokhlis1; M Mansour2 | ||
| 1Department of Mathematics, Faculty of Science, Ain Shams University, Cairo 11566, Egypt | ||
| 2Department of Basic Science, Faculty of Engineering, The British University in Egypt, Cairo 11837, Egypt | ||
| Abstract | ||
| The Marshall-Olkin extended modified inverse Rayleigh (MOEMIR) distribution, a new extension of the modified inverse Rayleigh} (MIR) distribution, is introduced as a member of a proposed \textit{Marshall-Olkin extended general inverse exponential (MOEGIE) family. This extension offers enhanced flexibility for modeling lifetime data. Statistical properties of the MOEGIE family are presented, and hence those of the MOEMIR distribution. Parameter estimation for the MOEMIR distribution is discussed under an adaptive Type-II progressive censoring scheme involving discrete uniformly distributed random removals. The parameters of the MOEMIR distribution are estimated using both maximum likelihood and Bayesian methods. The Bayesian estimation is refined under symmetric \textit{squared error loss} (SEL) and asymmetric \textit{linear exponential loss} (LINEX) functions, using a \textit{Metropolis-Hastings} (M-H) sampling method of the \textit{Markov chain Monte Carlo} (MCMC) technique. A simulation study is performed to highlight the obtained theoretical results. Finally, the utility of the MOEMIR distribution is demonstrated using a real-world dataset involving the remission times of patients with bladder cancer. | ||
| Keywords | ||
| Marshall-Olkin extended distribution; Modified inverse Rayleigh distribution; Maximum likelihood estimation; Bayesian estimation | ||
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