J. H. Blanchet, Nan Chen and P. W. Glynn, “Unbiased Monte Carlo computation of smooth functions of expectations via Taylor expansions,” 2015 Winter Simulation Conference (WSC), Huntington Beach, CA, 2015, pp. 360-367, doi: 10.1109/WSC.2015.7408178.
Abstract
Many Monte Carlo computations involve computing quantities that can be expressed as g(EX), where g is nonlinear and smooth, and X is an easily simulatable random variable. The nonlinearity of g makes the conventional Monte Carlo estimator for such quantities biased. In this paper, we show how such quantities can be estimated without bias. However, our approach typically increases the variance. Thus, our approach is primarily of theoretical interest in the above setting. However, our method can also be applied to the computation of the inner expectation associated with Eg ((EX|Z)), and in this setting, the application of this method can have a significant positive effect on improving the rate of convergence relative to conventional “nested schemes” for carrying out such calculations.
Authors
Jose H Blanchet, Nan Chen, Peter W Glynn
Publication date
2015/12/6
Conference
2015 Winter Simulation Conference (WSC)
Pages
360-367
Publisher
IEEE
