Bootstrap-based techniques for computing Monte Carlo system reliability confidence intervals in evaluation

In this paper, the usefulness of bootstrapping techniques for performing statistical inference of reliability measures is discussed. Bootstrap methods are presented for constructing confidence intervals of reliability indices. The bootstrap is a general re-sampling procedure which can be used to estimate the sampling distribution of statistics, requiring few assumptions, little modeling and analysis and which can be applied in an automatic way. Statistical significance is evaluated based on empirical distributions generated from an observed sample, for example, through a Monte Carlo simulation. The approach is illustrated with reference to the evaluation of confidence intervals of reliability indices of different network systems.