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

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.