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Abstract Details
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Activity Number:
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465
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Type:
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Contributed
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Date/Time:
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Wednesday, August 1, 2012 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistical Computing
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| Abstract - #304328 |
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Title:
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Sampling for Conditional Inference on Network Data
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Author(s):
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Jingfei Zhang*+ and Yuguo Chen
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Companies:
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University of Illinois at Urbana-Champaign and University of Illinois at Urbana-Champaign
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Address:
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609 W MAIN ST, URBANA, IL, 61801, United States
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Keywords:
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Counting problem ;
Exact test ;
Random graphs ;
Random networks ;
Sequential importance sampling
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Abstract:
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Random graphs with given vertex degrees have been widely used as a model for many real-world complex networks. We describe two sequential sampling methods for sampling networks with a given degree sequence. These samples can be used to approximate closely the null distributions of a number of test statistics involved in such networks, and provide an accurate estimate of the total number of networks with given vertex degrees. We apply our method to a range of examples to demonstrate its efficiency in real problems.
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