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Maximum product spacing is an effective method for parameter, reliability, and hazard function estimation of the generalized Rayleigh distribution under adaptive type-II progressive censoring schemes, and can be directly compared to maximum likelihood and Bayesian approaches. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

February 24, 2021Open Access

Adaptive Type-II Progressive Censoring Schemes based on Maximum Product Spacing with Application of Generalized Rayleigh Distribution

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Abstract

In this paper, parameters estimation for the generalized Rayleigh (GR) distribution are discussed under the adaptive type-II progressive censoring schemes based on maximum product spacing. A comparison studies with another methods as maximum likelihood, and Bayesian estimation by use Markov chain Monte Carlo (MCMC) are discussed. Also, reliability estimation and hazard function are obtained. A numerical study using real data and Monte Carlo Simulation are performed to compare between different methods.

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Almetwally et al. (Wed,) studied this question.

synapsesocial.com/papers/6a1c7477ecffbcc5fca15b9d https://doi.org/https://doi.org/10.6339/jds.201910_17(4).0010

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