Importance Sampling for Monte Carlo Estimation of Quantiles
P. W. Glynn
Proceedings of the Second International Workshop on Mathematical Methods in Stochastic Simulation and Experimental Design, 180-185 (1996)
This paper is concerned with applying importance sampling as a variance reduction tool for computing extreme quantiles. A central limit theorem is derived for each of four proposed importance sampling quantile estimators. Efficiency comparisons are provided in a certain asymptotic setting, using ideas from large deviation theory.