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

Kousik Maiti (), Suchandan Kayal () and Aditi Kar Gangopadhyay ()
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Kousik Maiti: Haldia Institute of Technology
Suchandan Kayal: National Institute of Technology Rourkela
Aditi Kar Gangopadhyay: Indian Institute of Technology Roorkee

Annals of Data Science, 2024, vol. 11, issue 5, No 13, 1798 pages

Abstract: Abstract 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.

Keywords: Uniqueness and existence property; Bayes estimates; MCMC method; Bootstrap confidence intervals; Mean squared error; 62F10; 62F15; 62N01; 62N02 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s40745-023-00477-1

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