15,214 research outputs found
Semiparametric Cointegrating Rank Selection
Some convenient limit properties of usual information criteria are given for cointegrating rank selection. Allowing for a nonparametric short memory component and using a reduced rank regression with only a single lag, standard information criteria are shown to be weakly consistent in the choice of cointegrating rank provided the penalty coefficient C_n -> infinity and C_n/n -> 0 as n -> infinity. The limit distribution of the AIC criterion, which is inconsistent, is also obtained. The analysis provides a general limit theory for semiparametric reduced rank regression under weakly dependent errors. The method does not require the specification of a full model, is convenient for practical implementation in empirical work, and is sympathetic with semiparametric estimation approaches to cointegration analysis. Some simulations results on finite sample performance of the criterion are reported.Cointegrating rank, Consistency, Information criteria, Model selection, Nonparametric, Short memory, Unit roots
Deep residual learning in CT physics: scatter correction for spectral CT
Recently, spectral CT has been drawing a lot of attention in a variety of
clinical applications primarily due to its capability of providing quantitative
information about material properties. The quantitative integrity of the
reconstructed data depends on the accuracy of the data corrections applied to
the measurements. Scatter correction is a particularly sensitive correction in
spectral CT as it depends on system effects as well as the object being imaged
and any residual scatter is amplified during the non-linear material
decomposition. An accurate way of removing scatter is subtracting the scatter
estimated by Monte Carlo simulation. However, to get sufficiently good scatter
estimates, extremely large numbers of photons is required, which may lead to
unexpectedly high computational costs. Other approaches model scatter as a
convolution operation using kernels derived using empirical methods. These
techniques have been found to be insufficient in spectral CT due to their
inability to sufficiently capture object dependence. In this work, we develop a
deep residual learning framework to address both issues of computation
simplicity and object dependency. A deep convolution neural network is trained
to determine the scatter distribution from the projection content in training
sets. In test cases of a digital anthropomorphic phantom and real water
phantom, we demonstrate that with much lower computing costs, the proposed
network provides sufficiently accurate scatter estimation
Time-aware topic recommendation based on micro-blogs
Topic recommendation can help users deal with the information overload issue in micro-blogging communities. This paper proposes to use the implicit information network formed by the multiple relationships among users, topics and micro-blogs, and the temporal information of micro-blogs to find semantically and temporally relevant topics of each topic, and to profile users' time-drifting topic interests. The Content based, Nearest Neighborhood based and Matrix Factorization models are used to make personalized recommendations. The effectiveness of the proposed approaches is demonstrated in the experiments conducted on a real world dataset that collected from Twitter.com
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