Abstract: The generalized extreme value distribution has attracted the attention of many scholars since it was proposed. It can be used to fit life data and is widely used in the fields of medical sciences, engineering and meteorology. In this paper, the Bayesian regression analysis under the three-parameter generalized extreme value model is studied under the current status data. Based on the location parameter of generalized extreme value distribution, the covariate are introduced, the Bayesian regression model of location parameter and survival time are established, and the MCMC method combining with Gibbs sampling and MH algorithm is used to draw sample from the posterior distribution of each parameter. The means of the posterior samples are used as the estimated value of the parameters. R software is used for numerical simulation to compare the performance of maximum likelihood estimation and Bayesian estimation, which illustrates that the parametric survival regression model fits the data well. Simulation results indicate that Bayesian estimation outperforms the maximum likelihood estimation. Finally, this method is applied to the lung tumor data of 144 male RFM mice, and some results are obtained.

Key words: current status data, generalized extreme value distribution, maximum likelihood estimation, Bayesian estimation, MCMC algorithm