Distribution Sensitivity Estimation and Its Applications
报告人： Yijie Peng, Peking University
We propose a generalized likelihood ratio method to estimate
the distribution sensitivities for the output process by simulating the
dynamics of the model. The distribution sensitivity estimator is used to
estimate unknown parameters in a stochastic model without assuming that
the likelihood function of the observations is available in closed form,
which allows fitting some complex stochastic models to the output data.
We also apply the distribution sensitivity estimator to compute the
distortion risk measure, as an alternative to expected utility, widely
used in behavioral economics and risk management. Asymptotic results for
the sensitivity estimator of the distortion risk measure have been
established by using a functional limit theory.
About the Speaker:
Speaker introduction: Dr. Yijie Peng is currently an assistant professor of the Department of Industrial Engineering and Management at Peking University (PKU). He received his Ph.D. from the Department of Management Science at Fudan University and his B.S. degree from the School of Mathematics at Wuhan University. Before joining PKU, he worked as an assistant professor at George Mason University, and postdoctoral scholar at Fudan University and R.H. Smith School of Business at University of Maryland at College Park. Many of his publications appear in high-quality journals including Operations Research, IEEE Transactions on Automatic Control, INFORMS Journal on Computing, Journal of Discrete Event Dynamic System, and Quantitative Finance. His research interests include stochastic modeling and analysis, simulation optimization, machine learning, data analytics, and healthcare.