On Progressively Censored Generalized X-Exponential Distribution: (Non) Bayesian Estimation with an Application to Bladder Cancer Data

This article addresses estimation of the parameters and reliability characteristics of a generalized X -Exponential distribution based on the progressive type-II censored sample. The maximum likelihood estimates (MLEs) are obtained. The uniqueness and existence of the MLEs are studied. The Bayes est...

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Bibliographic Details
Published in:Annals of data science 2024-10, Vol.11 (5), p.1761-1798
Main Authors: Maiti, Kousik, Kayal, Suchandan, Kar Gangopadhyay, Aditi
Format: Article
Language:English
Subjects:
Artificial Intelligence
Bladder cancer
Business and Management
Data science
Design
Economics
Error analysis
Experiments
Failure
Finance
Insurance
Management
Markov chains
Mathematics
Maximum likelihood estimates
Monte Carlo simulation
Parameter estimation
Probability distribution functions
Product reliability
Random variables
Statistics for Business
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Summary:This article addresses estimation of the parameters and reliability characteristics of a generalized X -Exponential distribution based on the progressive type-II censored sample. The maximum likelihood estimates (MLEs) are obtained. The uniqueness and existence of the MLEs are studied. The Bayes estimates are obtained under squared error and entropy loss functions. For computation of the Bayes estimates, Markov Chain Monte Carlo method is used. Bootstrap- t and bootstrap- p methods are used to compute the interval estimates. Further, a simulation study is performed to compare the performance of the proposed estimates. Finally, a real-life dataset is considered and analysed for illustrative purposes.
ISSN:2198-5804
2198-5812
DOI:10.1007/s40745-023-00477-1