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Semiparametric estimation for a class of time-inhomogenous diffusion processes
Copyright @ 2009 Institute of Statistical Science, Academia SinicaWe develop two likelihood-based approaches to semiparametrically estimate a class of time-inhomogeneous diffusion processes: log penalized splines (P-splines) and the local log-linear method. Positive volatility is naturally embedded and this positivity is not guaranteed in most existing diffusion models. We investigate different smoothing parameter selections. Separate bandwidths are used for drift and volatility estimation. In the log P-splines approach, different smoothness for different time varying coefficients is feasible by assigning different penalty parameters. We also provide theorems for both approaches and report statistical inference results. Finally, we present a case study using the weekly three-month Treasury bill data from 1954 to 2004. We find that the log P-splines approach seems to capture the volatility dip in mid-1960s the best. We also present an application to calculate a financial market risk measure called Value at Risk (VaR) using statistical estimates from log P-splines
Deciphering the 2016 U.S. Presidential Campaign in the Twitter Sphere: A Comparison of the Trumpists and Clintonists
In this paper, we study follower demographics of Donald Trump and Hillary
Clinton, the two leading candidates in the 2016 U.S. presidential race. We
build a unique dataset US2016, which includes the number of followers for each
candidate from September 17, 2015 to December 22, 2015. US2016 also includes
the geographical location of these followers, the number of their own followers
and, very importantly, the profile image of each follower. We use individuals'
number of followers and profile images to analyze four dimensions of follower
demographics: social status, gender, race and age. Our study shows that in
terms of social influence, the Trumpists are more polarized than the
Clintonists: they tend to have either a lot of influence or little influence.
We also find that compared with the Clintonists, the Trumpists are more likely
to be either very young or very old. Our study finds no gender affinity effect
for Clinton in the Twitter sphere, but we do find that the Clintonists are more
racially diverse.Comment: 4 pages, to appear in the 10th International AAAI Conference on Web
and Social Medi
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