Estimating the variance in case of undermodeling using bootstrap

Simulation based methods have gained interest in the signal processing community. In this article we propose an algorithm to estimate the probability density function of some statistic associated with an identified model in the case of undermodeling. With this algorithm, we are thus able to estimate the variance error of any statistic associated with the model. We also give a simulation example, which shows that the estimates are in very good agreement with Monte Carlo simulations.