Liu, Mengxiong

University of Michigan, Ann Arbor, MI, School of Informational & Library Studies, Ph.D., 1990 University of Denver, Denver, CO, Graduate School of Librarianship & Information Management, M.L.S., 1983 International Studies University, Shanghai, China, English Department, B.A., 1968https://scholarworks.sjsu.edu/erfa_bios/1273/thumbnail.jp

Remarks on minimal rational curves on moduli spaces of stable bundles

Let M be the moduli space of stable bundles of rank 2 and with fixed determinant \mathcal{L} of degree d on a smooth projective curve C of genus g>= 2. When g=3 and d is even, we prove, for any point [W]\in M, there is a minimal rational curve passing through [W], which is not a Hecke curve. This complements a theorem of Xiaotao Sun

New Liu Estimators for the Poisson Regression Model: Method and Application

A new shrinkage estimator for the Poisson model is introduced in this paper. This method is a generalization of the Liu (1993) estimator originally developed for the linear regression model and will be generalised here to be used instead of the classical maximum likelihood (ML) method in the presence of multicollinearity since the mean squared error (MSE) of ML becomes inflated in that situation. Furthermore, this paper derives the optimal value of the shrinkage parameter and based on this value some methods of how the shrinkage parameter should be estimated are suggested. Using Monte Carlo simulation where the MSE and mean absolute error (MAE) are calculated it is shown that when the Liu estimator is applied with these proposed estimators of the shrinkage parameter it always outperforms the ML. Finally, an empirical application has been considered to illustrate the usefulness of the new Liu estimators.Estimation; MSE; MAE; Multicollinearity; Poisson; Liu; Simulation

Finding Eyewitness Tweets During Crises

Disaster response agencies have started to incorporate social media as a source of fast-breaking information to understand the needs of people affected by the many crises that occur around the world. These agencies look for tweets from within the region affected by the crisis to get the latest updates of the status of the affected region. However only 1% of all tweets are geotagged with explicit location information. First responders lose valuable information because they cannot assess the origin of many of the tweets they collect. In this work we seek to identify non-geotagged tweets that originate from within the crisis region. Towards this, we address three questions: (1) is there a difference between the language of tweets originating within a crisis region and tweets originating outside the region, (2) what are the linguistic patterns that can be used to differentiate within-region and outside-region tweets, and (3) for non-geotagged tweets, can we automatically identify those originating within the crisis region in real-time