154,867 research outputs found
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
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