Abstract:

Simulation is a powerful numerical tool set for performance evaluation and optimization of stochastic systems. Successful implementation of this 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 steady-state simulation, parameter estimation using stochastic gradient descent, quantile estimation etc. The proposed procedure provides statistical guarantees for the quality of the estimators. This is joint work with Peter Glynn.

Bio:

Jing Dong is an assistant professor in the Decision, Risk and Operations Division at Columbia Business School. Her primary research interests are at the interface of applied probability and service operations management. She also develop efficient simulation algorithms to facilitate better decision-making in complex stochastic systems. She got her PhD in Operations Research from Columbia University.

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