Inference and optimal design on step-stress partially accelerated life test for hybrid system with masked data

Under Type-II progressively hybrid censoring, this paper discusses statistical inference and optimal design on step-stress partially accelerated life test for hybrid system in presence of masked data. It is assumed that the lifetime of the component in hybrid systems follows independent and identical modified Weibull distributions. The maximum likelihood estimations (MLEs) of the unknown parameters, acceleration factor and reliability indexes are derived by using the Newton-Raphson algorithm. The asymptotic variance-covariance matrix and the approximate confidence intervals are obtained based on normal approximation to the asymptotic distribution of MLEs of model parameters. Moreover, two bootstrap confidence intervals are constructed by using the parametric bootstrap method. The optimal time of changing stress levels is determined under D-optimality and A-optimality criteria. Finally, the Monte Carlo simulation study is carried out to illustrate the proposed procedures.