Robustness properties of perturbation analysis estimators for discrete event systems with unknown distributions

Sample-path-based stochastic gradient estimators for performance measures of discrete event systems rely on the assumption that a probability distribution of the random vector of interest (e.g. a service or interarrival time sequence) is given. The authors address the issue of dealing with unknown probability distributions and investigate the `robustness´ of such estimators with respect to possibly erroneous distribution choices. It is shown that infinitesimal perturbation analysis can be robust in this sense, and, in some cases, provides distribution-independent estimates. Comparisons with other gradient estimators are provided