Comput Stat DOI 10.1007/s00180-015-0586-6 ORIGINAL PAPER A stochastic expectation-maximization algorithm for the analysis of system lifetime data with known signature Yandan Yang 1 · Hon Keung Tony Ng 1 · Narayanaswamy Balakrishnan 2 Received: 28 August 2014 / Accepted: 8 May 2015 © Springer-Verlag Berlin Heidelberg 2015 Abstract Statistical estimation of the model parameters of component lifetime distri- bution based on system lifetime data with known system structure is discussed here. We propose the use of stochastic expectation-maximization (SEM) algorithm for obtain- ing the maximum likelihood estimates of model parameters based on complete and censored system lifetimes. Different ways of implementing the SEM algorithm are also studied. We have shown that the proposed methods are feasible and are easy to implement for various families of component lifetime distributions. The proposed methodologies are then illustrated with two popular lifetime models—the Weibull and Birnbaum-Saunders distributions. Monte Carlo simulation is then used to com- pare the performance of the proposed methods with the corresponding estimation by direct maximization. Finally, two illustrative examples are presented along with some concluding remarks. Keywords Expectation-maximization algorithm · Maximum likelihood estimation · Monte Carlo simulation · Reliability data · Type-II censoring Electronic supplementary material The online version of this article (doi:10.1007/s00180-015-0586-6) contains supplementary material, which is available to authorized users. B Hon Keung Tony Ng ngh@mail.smu.edu 1 Department of Statistical Science, Southern Methodist University, Dallas, TX 75275-0332, USA 2 Department of Mathematics and Statistics, McMaster University, Hamilton, Ontario L8S 4K1, Canada 123