Statistical inference for generalized exponential distribution under improved adaptive Type-II progressive censoring

Articles in Press

Document Type : Original Article

Authors
Manoj Chacko
Shilpa S Dev
Department of Statistics, University of Kerala, Trivandrum, India
10.22034/jirss.2026.2077492.1165
Abstract
This paper explores an approach to analyse the estimation of parameters of generalized exponential distribution using an improved adaptive type-II progressive censoring scheme. The point and interval estimations, utilizing two classical estimation methods maximum likelihood and maximum product of spacing are employed to estimate the unknown parameters, as well as the reliability and hazard rate functions. The approximate confidence intervals for these quantities are derived using the asymptotic normality of the maximum likelihood and maximum product of spacing methods. Bayesian estimations are explored using Markov Chain Monte Carlo (MCMC) techniques, based on two established approaches. The Bayesian estimation is examined under both symmetric and asymmetric loss functions. A Monte Carlo simulation study is conducted to compare the performance of the proposed estimates, and the effectiveness of the estimation approach is illustrated using a real dataset.
Keywords
Improved adaptive type-II progressive censoring
generalized exponential distribution
maximum likelihood estimation
maximum product spacing estimation
Bayes estimation
Markov Chain Monte Carlo method
Subjects
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Journal of the Iranian Statistical Society

Articles in Press, Accepted Manuscript
Available Online from 21 July 2026

  • Receive Date 14 November 2025
  • Revise Date 04 July 2026
  • Accept Date 08 July 2026