Randomized Estimators for Time IntegralsP. W. Glynn Technical Report, Mathematics Research Center, University of Wisconsin, Madison (1983) Let {X(t): t≥0} be a realvalued stochastic process and set α = ∫ X(t) G(dt), where G is a (nonrandom) distribution func t ion. I f t hE support of G is large, standard Monte carlo technique s for estimating α are inefficient, since X must be simulated over the entire support o f G. To avoid this difficulty, randomization schemes are derived that require simulation of X over random subsets of the support of G. Largesample behavior of randomized estimators is studied in detail. Some variance reduction techniques are also presented.d
