prediction for the future system failures based on type-ii censored coherent systems data under a proportional hazard model

In this paper‎, ‎we consider a k-component coherent system while the system lifetimes are observed‎, ‎the system structure is known and the component lifetime follows the proportional hazard rate model‎. ‎we discuss the prediction problem based on type-ii censored coherent system lifetime data‎. ‎for predicting the future system failures‎, ‎we obtain the maximum likelihood predictor‎, ‎the best unbiased predictor‎, ‎the conditional median predictor and the bayesian predictors‎. ‎as it seems that the integrals of the bayes prediction do not possess closed forms‎, ‎the metropolis-hastings method is applied to approximating these integrals‎. ‎different interval predictors based on classical and bayesian approaches are derived‎. ‎a numerical example is presented to illustrate the prediction methods used in this paper‎. ‎a monte carlo simulation study is performed to evaluate and compare the performances of different prediction methods.