Parameter estimation of generalized Pareto distributionunder progressively type-II censored data

Progressively type-II censored data is widely used in survival analysis. Therefore, the estimation forms of maximum likelihood estimation and bayes estimation for scale parameters in generalized Pareto distribution are derived under progressively type-ii censored data. Under the square loss function, based on MCMC method and Lindley approximation method, the Bayes estimation of generalized Pareto distribution scale parameters is given. The results show that Bayesian estimation is better than maximum likelihood estimation, and Bayesian estimation under MCMC posterior sampling is better than Lindley approximation.