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Comparative estimation techniques for exponential-Rayleigh models under adaptive type-II progressive censoring
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DOI：10.1016/j.cie.2025.111788.png)
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Abstract
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• The maximum likelihood and Bayesian methods are developed to estimate the parameters of the exponential–Rayleigh model under adaptive progressive censoring. • Bayesian estimation is carried out using squared error and entropy loss functions, and different types of confidence intervals are provided. • Monte Carlo simulations are used to check how well the proposed estimation methods perform. • A complete real dataset is used and an ATPC scheme is applied to show how the proposed estimation methods work for ATPC data.
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