MONTE CARLO SIMULATION WITH CENSORED SAMPLING

We consider Monte Carlo simulation in a setting where the samples are subject to random censoring. Such censoring occurs in settings as varied and diverse as perimeter protection, survival analysis, and electro-magnetic spectrum monitoring. We introduce and analyze two estimators: one based on empirical likelihood methods and another rooted in control variates ideas. We show that the proposed estimators can dramatically reduce the estimator variance in relation to the crude Monte Carlo estimator while not sacrificing computational speed.