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Poster
Fast Resampling Weighted v-Statistics
Chunxiao Zhou · jiseong Park · Yun Fu
Tue Dec 04 07:00 PM -- 12:00 AM (PST) @ Harrah’s Special Events Center 2nd Floor
In this paper, a novel, computationally fast, and alternative algorithm for com- puting weighted v-statistics in resampling both univariate and multivariate data is proposed. To avoid any real resampling, we have linked this problem with finite group action and converted it into a problem of orbit enumeration. For further computational cost reduction, an efficient method is developed to list all orbits by their symmetry order and calculate all index function orbit sums and data function orbit sums recursively. The computational complexity analysis shows reduction in the computational cost from n! or nn level to low-order polynomial level.
Author Information
Chunxiao Zhou (NIH (National Institutes of Health))
jiseong Park
Yun Fu (Northeastern University)
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2012 Spotlight: Fast Resampling Weighted v-Statistics »
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