Seminar Series: Fall 2014
The seminar series will usually take place on Fridays in a Business Building room, but the exact time and location could be different due to a variety of factors including room availability in the Business Building. For actual direction to the Business Building, please see campus map. For additional information contact Dr. Kefeng Xu, (210) 458-5388.
Friday, Fri, Oct 10 2014, 3-4pm, Business Building, BB 3.03.20.
Presenter: Baogui Xin, Associate Professor, Department of Management Science, College of Economics and Management, Shandong University of Science and Technology
Presentation Title: A differential oligopoly dual-game with sticky goods prices and water-right trading
Abstract: A dynamic oligopoly dual-game is studied in which goods prices are sticky and water-right trading occurs. Unlike studies in the literature, oligopolies in this study exist not only in the goods game but also in the water-right game. Static, closed-loop, open-loop and feedback equilibrium conditions are analyzed. The evolution complexity of optimal trajectories is also considered. The impact of oligopoly competition on the social welfare is finally examined. [Keywords: Differential Game; Dynamic Optimization; Oligopoly Competition; Social Welfare.]
- Speaker Bio: Professor Xin is currently a visiting scholar at the Department of Management Science and Statistics, College of Business, UTSA.
Friday, Oct. 31, 2014, 3-4pm, Business Building 4.02.10 (Executive Conference Room)
Presenter: Ming-Hui Chen Professor Department of Statistics, University of Connecticut
Presentation Title: Online Updating of Statistical Inference in the Big Data Setting
Abstract: We present statistical methods for big data arising from online analytical processing, where large amounts of data arrive in streams and require fast analysis without storage/access to the historical data. In particular, we develop iterative estimating algorithms and statistical inferences for linear models and estimating equations that update as new data arrive. The online updating framework in the linear model setting introduces predictive residuals that can be used to test the goodness-of-fit of the hypothesized model. We also present a new online-updating estimator under the estimating equation setting. In simulation studies and real data applications, our estimator compares favorably with competing approaches. [This is joint work with Elizabeth D. Schifano, Jing Wu, Chun Wang, and Jun Yan.]