BAYESIAN PARAMETER ESTIMATION OF WEIBULL DISTRIBUTION WITH MULTIPLE CHANGE POINTS FOR RANDOM CENSORING TEST MODEL WITH INCOMPLETE INFORMATION

Abstract

The complete-data likelihood function of Weibull distribution with multiple change points for IIRCT is obtained by filling in missing life data using inverse transformation method. The full conditional distributions of change-point positions and other unknown parameters are obtained. Every parameter is sampled by Gibbs sampler. and the means of Gibbs samples are taken as Bayesian estimations of the parameters. The concrete steps of MCMC methods are given. The random simulation results show that the estimations are fairly accurate and the effect is good.

Keywords

• Complete-data likelihood function;Full conditional distribution;MCMC method;Gibbs sampling;Metropolis-Hastings algorithm