Bayesian Inference for Inverse Power Exponentiated Pareto Distribution Using Progressive Type-II Censoring with Application to Flood-Level Data Analysis

Progressive type-II (Prog-II) censoring schemes are gaining traction in estimating the parameters, and reliability characteristics of lifetime distributions. The focus of this paper is to enhance the accuracy and reliability of such estimations for the inverse power exponentiated Pareto (IPEP) distr...

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Bibliographic Details
Published in:Symmetry (Basel) 2024-03, Vol.16 (3), p.309
Main Authors: Khalifa, Eman H., Ramadan, Dina A., Alqifari, Hana N., El-Desouky, Beih S.
Format: Article
Language:English
Subjects:
Bayes estimation
Bayesian analysis
bootstrap confidence interval
Censorship
Data analysis
Decision-making
Electric power distribution
Estimation
Experiments
Failure
Failure times
Floods
Hypothesis testing
inverse power exponentiated Pareto distribution
Lifetime
Literature reviews
Markov chains
Markov processes
Mathematical models
maximum likelihood estimation
Maximum likelihood estimators
Medical advice systems
Metropolis–Hasting algorithm
Monte Carlo method
Monte Carlo simulation
Parameters
Performance evaluation
progressive type-II censoring
Random variables
Reliability
Statistical inference
Statistical methods
Survival analysis
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Summary:Progressive type-II (Prog-II) censoring schemes are gaining traction in estimating the parameters, and reliability characteristics of lifetime distributions. The focus of this paper is to enhance the accuracy and reliability of such estimations for the inverse power exponentiated Pareto (IPEP) distribution, a flexible extension of the exponentiated Pareto distribution suitable for modeling engineering and medical data. We aim to develop novel statistical inference methods applicable under Prog-II censoring, leading to a deeper understanding of failure time behavior, improved decision-making, and enhanced overall model reliability. Our investigation employs both classical and Bayesian approaches. The classical technique involves constructing maximum likelihood estimators of the model parameters and their bootstrap covariance intervals. Using the Gibbs process constructed by the Metropolis–Hasting sampler technique, the Markov chain Monte Carlo method provides Bayesian estimates of the unknown parameters. In addition, an actual data analysis is carried out to examine the estimation process’s performance under this ideal scheme.
ISSN:2073-8994
2073-8994
DOI:10.3390/sym16030309