Time: 11:00am to 12:30pm
Venue: Room 6-207, 6/F, Lau Ming Wai Academic Building
We derive a generalized likelihood ratio method to estimate any distribution sensitivity in a unified form. Then, the distribution sensitivities are used to in a gradient-based simulated maximum likelihood estimation (GSMLE) to estimate unknown parameters in a stochastic model without assuming that the likelihoods of the observations are available in closed form. GSMLE can direct fit the underlying stochastic model to the output data, which opens the possibility of extending data-driven ideas to complex (causal) stochastic models. In addition, GSMLE can efficiently address the calibration of hidden Markov model, which has been considered as a difficult problem in statistics and econometrics.
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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 sensitivity analysis and ranking and selection in the field of simulation optimization, with applications in manufacturing and financial engineering. |