TITLE:  A New Approach to Sequential Stopping for Stochastic Simulation

ABSTRACT:

Simulation is a powerful numerical tool set for performance evaluation and optimization of stochastic systems. Successful implementation of this numerical approximation scheme requires one being able to assess the quality of the estimators and control the estimation errors. In this talk, I will present a new sequential stopping framework for stochastic simulation problems in which variance estimation is difficult. Examples include estimating smooth function of expectations, steady-state simulation, and various stochastic optimization problems. The proposed procedure guarantees the accuracy of the estimator and achieves the desired reliability asymptotically. (Joint work with Peter Glynn.)

Bio:  Jing Dong is an assistant Professor in the Department of Industrial Engineering and Management Sciences at Northwestern University. Her research interests are applied probability, stochastic simulation, and service operations management. She got her Phd in Operations Research from Columbia University.